RSS 2020 — all the papers and videos
RSS 2020 was held virtually this year, from the RSS Pioneers Workshop on July 11 to the Paper Awards and Farewell on July 16. Many talks are now available online, including 103 accepted papers, each presented as an online Spotlight Talk on the RSS Youtube channel, and of course the plenaries and much of the workshop content as well. We’ve tried to link here to all of the goodness from RSS 2020.
The RSS Keynote on July 15 was delivered by Josh Tenenbaum, Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences, CSAIL. Titled “It’s all in your head: Intuitive physics, planning, and problem-solving in brains, minds and machines”.
Abstract: I will overview what we know about the human mind’s internal models of the physical world, including how these models arise over evolution and developmental learning, how they are implemented in neural circuitry, and how they are used to support planning and rapid trial-and-error problem-solving in tool use and other physical reasoning tasks. I will also discuss prospects for building more human-like physical common sense in robots and other AI systems.
RSS 2020 introduces the new RSS Test of Time Award given to highest impact papers published at RSS (and potentially journal versions thereof) from at least ten years ago. Impact may mean that it changed how we think about problems or about robotic design, that it brought fully new problems to the attention of the community, or that it pioneered new approach to robotic design or problem solving. With this award, RSS generally wants to foster the discussion of the long term development of our field. The award is an opportunity to reflect on and discuss the past, which is essential to make progress in the future. The awardee’s keynote is therefore complemented with a Test of Time Panel session devoted to this important discussion.
This year’s Test of Time Awards goes to the pair of papers for pioneering an information smoothing approach to the SLAM problem via square root factorization, its interpretation as a graphical model, and the widely-used GTSAM free software repository.
- Frank Dellaert: Square Root SAM. Proceedings of Robotics: Science and Systems I, 2005.
- Frank Dellaert and Michael Kaess: Square Root SAM: Simultaneous localization and mapping via square root information smoothing. International Journal of Robotics Research, 25, 1181–1203, 2006.
Abstract: Many estimation, planning and optimal control problems in robotics have an optimization problem at their core. In most of these optimization problems, the objective function is composed of many different factors or terms that are local in nature, i.e., they only depend on a small subset of the variables. 10 years ago the Square Root SAM papers identified factor graphs as a particularly insightful way of modeling this locality structure. Since then we have realized that factor graphs can represent a wide variety of problems across robotics, expose opportunities to improve computational performance, and are beneficial in designing and thinking about how to model a problem, even aside from performance considerations. Many of these principles have been embodied in our evolving open source package GTSAM, which puts factor graphs front and central, and which has been used with great success in a number of state of the art robotics applications. We will also discuss where factor graphs, in our opinion, can break in
The RSS 2020 Plenary Sessions highlighted Early Career Awards for researchers, Byron Boots, Luca Carlone and Jeanette Bohg. Byron Boots is an Associate Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. Luca Carlone is the Charles Stark Draper Assistant Professor in the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology, and a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS). Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University.
Title: Perspectives on Machine Learning for Robotics
Abstract: Recent advances in machine learning are leading to new tools for designing intelligent robots: functions relied on to govern a robot’s behavior can be learned from a robot’s interaction with its environment rather than hand-designed by an engineer. Many machine learning methods assume little prior knowledge and are extremely flexible, they can model almost anything! But, this flexibility comes at a cost. The same algorithms are often notoriously data hungry and computationally expensive, two problems that can be debilitating for robotics. In this talk I’ll discuss how machine learning can be combined with prior knowledge to build effective solutions to robotics problems. I’ll start by introducing an online learning perspective on robot adaptation that unifies well-known algorithms and suggests new approaches. Along the way, I’ll focus on the use of simulation and expert advice to augment learning. I’ll discuss how imperfect models can be leveraged to rapidly update simple control policies and imitation can accelerate reinforcement learning. I will also show how we have applied some of these ideas to an autonomous off-road racing task that requires impressive sensing, speed, and agility to complete.
Title: The Future of Robot Perception: Certifiable Algorithms and Real-time High-level Understanding
Abstract: Robot perception has witnessed an unprecedented progress in the last decade. Robots are now able to detect objects and create large-scale maps of an unknown environment, which are crucial capabilities for navigation, manipulation, and human-robot interaction. Despite these advances, both researchers and practitioners are well aware of the brittleness of current perception systems, and a large gap still separates robot and human perception.
This talk discusses two efforts targeted at bridging this gap. The first focuses on robustness. I present recent advances in the design of certifiable perception algorithms that are robust to extreme amounts of noise and outliers and afford performance guarantees. I present fast certifiable algorithms for object pose estimation: our algorithms are “hard to break” (e.g., are robust to 99% outliers) and succeed in localizing objects where an average human would fail. Moreover, they come with a “contract” that guarantees their input-output performance. I discuss the foundations of certifiable perception and motivate how these foundations can lead to safer systems.
The second effort targets high-level understanding. While humans are able to quickly grasp both geometric, semantic, and physical aspects of a scene, high-level scene understanding remains a challenge for robotics. I present our work on real-time metric-semantic understanding and 3D Dynamic Scene Graphs. I introduce the first generation of Spatial Perception Engines, that extend the traditional notions of mapping and SLAM, and allow a robot to build a “mental model” of the environment, including spatial concepts (e.g., humans, objects, rooms, buildings) and their relations at multiple levels of abstraction.
Certifiable algorithms and real-time high-level understanding are key enablers for the next generation of autonomous systems, that are trustworthy, understand and execute high-level human instructions, and operate in large dynamic environments and over and extended period of time
Title: A Tale of Success and Failure in Robotics Grasping and Manipulation
Abstract: In 2007, I was a naïve grad student and started to work on vision-based robotic grasping. I had no prior background in manipulation, kinematics, dynamics or control. Yet, I dove into the field by re-implementing and improving a learning-based method. While making some contributions, the proposed method also had many limitations partly due to the way the problem was framed. Looking back at the entire journey until today, I find that I have learned the most about robotic grasping and manipulation from observing failures and limitations of existing approaches — including my own. In this talk, I want to highlight how these failures and limitations have shaped my view on what may be some of the underlying principles of autonomous robotic manipulation. I will emphasise three points. First, perception and prediction will always be noisy, partial and sometimes just plain wrong. Therefore, one focus of my research is on methods that support decision-making under uncertainty due to noisy sensing, inaccurate models and hard-to-predict dynamics. To this end, I will present a robotic system that demonstrates the importance of continuous, real-time perception and its tight integration with reactive motion generation methods. I will also talk about work that funnels uncertainty by enabling robots to exploit contact constraints during manipulation.
Second, a robot has many more sensors than just cameras and they all provide complementary information. Therefore, one focus of my research is on methods that can exploit multimodal information such as vision and touch for contact-rich manipulation. It is non-trivial to manually design a manipulation controller that combines modalities with very different characteristics. I will present work that uses self-supervision to learn a compact and multimodal representation of visual and haptic sensory inputs, which can then be used to improve the sample efficiency of policy learning. And third, choosing the right robot action representation has a large influence on the success of a manipulation policy, controller or planner. While believing many years that inferring contact points for robotic grasping is futile, I will present work that convinced me otherwise. Specifically, this work uses contact points as an abstraction that can be re-used by a diverse set of robot hands.
Inclusion@RSS is excited to host a panel “On the Future of Robotics” to discuss how we can have an inclusive robotics community and its impact on the future of the field. Moderator: Matt Johnson-Roberson (University of Michigan) with Panelists: Tom Williams (Colorado School of Mines), Eduard Fosch-Villaronga (Leiden University), Lydia Tapia (University of New Mexico), Chris Macnab (University of Calgary), Adam Poulsen (Charles Sturt University), Chad Jenkins (University of Michigan), Kendall Queen (University of Pennsylvania), Naveen Kuppuswamy (Toyota Research Institute).
The RSS community is committed to increasing the participation of groups traditionally underrepresented in robotics (including but not limited to: women, LGBTQ+, underrepresented minorities, and people with disabilities), especially people early in their studies and career. Such efforts are crucial for increasing research capacity, creativity, and broadening the impact of robotics research.
The RSS Pioneers Workshop for senior Ph.D. students and postdocs, was modelled on the highly successful HRI Pioneers Workshop and took place on Saturday July 11. The goal of RSS Pioneers is to bring together a cohort of the world’s top early career researchers to foster creativity and collaborations surrounding challenges in all areas of robotics, as well as to help young researchers navigate their next career stages. The workshop included a mix of research and career talks from senior scholars in the field from both academia and industry, research presentations from attendees and networking activities, with a poster session where Pioneers will get a chance to externally showcase their research.
Content from the various workshops on July 12 and 13 may be available through the various workshop websites.
RSS 2020 Accepted Workshops
WS1-2 Reacting to contact: Enabling transparent interactions through intelligent sensing and actuation
WS1-3 Certifiable Robot Perception: from Global Optimization to Safer Robots
WS1-4 Advancing the State of Machine Learning for Manufacturing Robotics
WS1-5Advances and Challenges in Imitation Learning for Robotics
WS1-62nd Workshop on Closing the Reality Gap in Sim2Real Transfer for Robotics
WS1-8Perception and Control for Fast and Agile Super-Vehicles II
WS1-10Heterogeneous Multi-Robot Task Allocation and Coordination
WS1-11Learning (in) Task and Motion Planning
WS1-12Performing Arts Robots & Technologies, Integrated (PARTI)
WS1-13Robots in the Wild: Challenges in Deploying Robust Autonomy for Robotic Exploration
WS1-14Emergent Behaviors in Human-Robot Systems
Monday, July 13
WS2–1Interaction and Decision-Making in Autonomous Driving
WS2–22nd RSS Workshop on Robust Autonomy: Tools for Safety in Real-World Uncertain Environments
WS2–3AI & Its Alternatives in Assistive & Collaborative Robotics
WS2–4Benchmarking Tools for Evaluating Robotic Assembly of Small Parts
WS2–5Good Citizens of Robotics Research
WS2–6Structured Approaches to Robot Learning for Improved Generalization
WS2–7Explainable and Trustworthy Robot Decision Making for Scientific Data Collection
WS2–8Closing the Academia to Real-World Gap in Service Robotics
WS2–9Visuotactile Sensors for Robust Manipulation: From Perception to Control
WS2–10Self-Supervised Robot Learning
WS2–11Power On and Go Robots: ‘Out-of-the-Box’ Systems for Real-World Applications
WS2–12Workshop on Visual Learning and Reasoning for Robotic Manipulation
WS2–13Action Representations for Learning in Continuous Control
RSS 2020 Accepted Papers
1Planning and Execution using Inaccurate Models with Provable GuaranteesAnirudh Vemula (Carnegie Mellon University)*; Yash Oza (CMU); J. Bagnell (Aurora Innovation); Maxim Likhachev (CMU)
2Swoosh! Rattle! Thump! — Actions that SoundDhiraj Gandhi (Carnegie Mellon University)*; Abhinav Gupta (Carnegie Mellon University); Lerrel Pinto (NYU/Berkeley)
3Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene ImageDanny Driess (Machine Learning and Robotics Lab, University of Stuttgart)*; Jung-Su Ha (); Marc Toussaint ()
4Elaborating on Learned Demonstrations with Temporal Logic SpecificationsCraig Innes (University of Edinburgh)*; Subramanian Ramamoorthy (University of Edinburgh)
5Non-revisiting Coverage Task with Minimal Discontinuities for Non-redundant ManipulatorsTong Yang (Zhejiang University)*; Jaime Valls Miro (University of Technology Sydney); Yue Wang (Zhejiang University); Rong Xiong (Zhejiang University)
6LatticeNet: Fast Point Cloud Segmentation Using Permutohedral LatticesRadu Alexandru Rosu (University of Bonn)*; Peer Schütt (University of Bonn); Jan Quenzel (University of Bonn); Sven Behnke (University of Bonn)
7A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with UncertaintyValentin Peretroukhin (University of Toronto)*; Matthew Giamou (University of Toronto); W. Nicholas Greene (MIT); David Rosen (MIT Laboratory for Information and Decision Systems); Jonathan Kelly (University of Toronto); Nicholas Roy (MIT)
8Leading Multi-Agent Teams to Multiple Goals While Maintaining CommunicationBrian Reily (Colorado School of Mines)*; Christopher Reardon (ARL); Hao Zhang (Colorado School of Mines)
9OverlapNet: Loop Closing for LiDAR-based SLAMXieyuanli Chen (Photogrammetry & Robotics Lab, University of Bonn)*; Thomas Läbe (Institute for Geodesy and Geoinformation, University of Bonn); Andres Milioto (University of Bonn); Timo Röhling (Fraunhofer FKIE); Olga Vysotska (Autonomous Intelligent Driving GmbH); Alexandre Haag (AID); Jens Behley (University of Bonn); Cyrill Stachniss (University of Bonn)
10The Dark Side of Embodiment — Teaming Up With Robots VS Disembodied AgentsFilipa Correia (INESC-ID & University of Lisbon)*; Samuel Gomes (IST/INESC-ID); Samuel Mascarenhas (INESC-ID); Francisco S. Melo (IST/INESC-ID); Ana Paiva (INESC-ID U of Lisbon
11Shared Autonomy with Learned Latent ActionsHong Jun Jeon (Stanford University)*; Dylan Losey (Stanford University); Dorsa Sadigh (Stanford)
12Regularized Graph Matching for Correspondence Identification under Uncertainty in Collaborative PerceptionPeng Gao (Colorado school of mines)*; Rui Guo (Toyota Motor North America); Hongsheng Lu (Toyota Motor North America); Hao Zhang (Colorado School of Mines)
13Frequency Modulation of Body Waves to Improve Performance of Limbless RobotsBaxi Zhong (Goergia Tech)*; Tianyu Wang (Carnegie Mellon University); Jennifer Rieser (Georgia Institute of Technology); Abdul Kaba (Morehouse College); Howie Choset (Carnegie Melon University); Daniel Goldman (Georgia Institute of Technology)
14Self-Reconfiguration in Two-Dimensions via Active Subtraction with Modular RobotsMatthew Hall (The University of Sheffield)*; Anil Ozdemir (The University of Sheffield); Roderich Gross (The University of Sheffield)
15Singularity Maps of Space Robots and their Application to Gradient-based Trajectory PlanningDavide Calzolari (Technical University of Munich (TUM), German Aerospace Center (DLR))*; Roberto Lampariello (German Aerospace Center); Alessandro Massimo Giordano (Deutches Zentrum für Luft und Raumfahrt)
16Grounding Language to Non-Markovian Tasks with No Supervision of Task SpecificationsRoma Patel (Brown University)*; Ellie Pavlick (Brown University); Stefanie Tellex (Brown University)
17Fast Uniform Dispersion of a Crash-prone SwarmMichael Amir (Technion — Israel Institute of Technology)*; Freddy Bruckstein (Technion)
18Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual PerceptionMd Jahidul Islam (University of Minnesota Twin Cities)*; Peigen Luo (University of Minnesota-Twin Cities); Junaed Sattar (University of Minnesota)
19Collision Probabilities for Continuous-Time Systems Without SamplingKristoffer Frey (MIT)*; Ted Steiner (Charles Stark Draper Laboratory, Inc.); Jonathan How (MIT)
20Event-Driven Visual-Tactile Sensing and Learning for RobotsTasbolat Taunyazov (National University of Singapore); Weicong Sng (National University of Singapore); Brian Lim (National University of Singapore); Hian Hian See (National University of Singapore); Jethro Kuan (National University of Singapore); Abdul Fatir Ansari (National University of Singapore); Benjamin Tee (National University of Singapore); Harold Soh (National University Singapore)*
21Resilient Distributed Diffusion for Multi-Robot Systems Using CenterpointJIANI LI (Vanderbilt University)*; Waseem Abbas (Vanderbilt University); Mudassir Shabbir (Information Technology University); Xenofon Koutsoukos (Vanderbilt University)
22Pixel-Wise Motion Deblurring of Thermal VideosManikandasriram Srinivasan Ramanagopal (University of Michigan)*; Zixu Zhang (University of Michigan); Ram Vasudevan (University of Michigan); Matthew Johnson Roberson (University of Michigan)
23Controlling Contact-Rich Manipulation Under Partial ObservabilityFlorian Wirnshofer (Siemens AG)*; Philipp Sebastian Schmitt (Siemens AG); Georg von Wichert (Siemens AG); Wolfram Burgard (University of Freiburg)
24AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human VideosLaura Smith (UC Berkeley)*; Nikita Dhawan (UC Berkeley); Marvin Zhang (UC Berkeley); Pieter Abbeel (UC Berkeley); Sergey Levine (UC Berkeley)
25Provably Constant-time Planning and Re-planning for Real-time Grasping Objects off a Conveyor BeltFahad Islam (Carnegie Mellon University)*; Oren Salzman (Technion); Aditya Agarwal (CMU); Likhachev Maxim (Carnegie Mellon University)
26Online IMU Intrinsic Calibration: Is It Necessary?Yulin Yang (University of Delaware)*; Patrick Geneva (University of Delaware); Xingxing Zuo (Zhejiang University); Guoquan Huang (University of Delaware)
27A Berry Picking Robot With A Hybrid Soft-Rigid Arm: Design and Task Space ControlNaveen Kumar Uppalapati (University of Illinois at Urbana Champaign)*; Benjamin Walt ( University of Illinois at Urbana Champaign); Aaron Havens (University of Illinois Urbana Champaign); Armeen Mahdian (University of Illinois at Urbana Champaign); Girish Chowdhary (University of Illinois at Urbana Champaign); Girish Krishnan (University of Illinois at Urbana Champaign)
28Iterative Repair of Social Robot Programs from Implicit User Feedback via Bayesian InferenceMichael Jae-Yoon Chung (University of Washington)*; Maya Cakmak (University of Washington)
29Cable Manipulation with a Tactile-Reactive GripperSiyuan Dong (MIT); Shaoxiong Wang (MIT); Yu She (MIT)*; Neha Sunil (Massachusetts Institute of Technology); Alberto Rodriguez (MIT); Edward Adelson (MIT, USA)
30Automated Synthesis of Modular Manipulators’ Structure and Control for Continuous Tasks around ObstaclesThais Campos de Almeida (Cornell University)*; Samhita Marri (Cornell University); Hadas Kress-Gazit (Cornell)
31Learning Memory-Based Control for Human-Scale Bipedal LocomotionJonah Siekmann (Oregon State University)*; Srikar Valluri (Oregon State University); Jeremy Dao (Oregon State University); Francis Bermillo (Oregon State University); Helei Duan (Oregon State University); Alan Fern (Oregon State University); Jonathan Hurst (Oregon State University)
32Multi-Fidelity Black-Box Optimization for Time-Optimal Quadrotor ManeuversGilhyun Ryou (Massachusetts Institute of Technology)*; Ezra Tal (Massachusetts Institute of Technology); Sertac Karaman (Massachusetts Institute of Technology)
33Manipulation Trajectory Optimization with Online Grasp Synthesis and SelectionLirui Wang (University of Washington)*; Yu Xiang (NVIDIA); Dieter Fox (NVIDIA Research / University of Washington)
34VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric ManipulationRyan Hoque (UC Berkeley)*; Daniel Seita (University of California, Berkeley); Ashwin Balakrishna (UC Berkeley); Aditya Ganapathi (University of California, Berkeley); Ajay Tanwani (UC Berkeley); Nawid Jamali (Honda Research Institute); Katsu Yamane (Honda Research Institute); Soshi Iba (Honda Research Institute); Ken Goldberg (UC Berkeley)
35Spatial Action Maps for Mobile ManipulationJimmy Wu (Princeton University)*; Xingyuan Sun (Princeton University); Andy Zeng (Google); Shuran Song (Columbia University); Johnny Lee (Google); Szymon Rusinkiewicz (Princeton University); Thomas Funkhouser (Princeton University)
36Generalized Tsallis Entropy Reinforcement Learning and Its Application to Soft Mobile RobotsKyungjae Lee (Seoul National University)*; Sungyub Kim (KAIST); Sungbin Lim (UNIST); Sungjoon Choi (Disney Research); Mineui Hong (Seoul National University); Jaein Kim (Seoul National University); Yong-Lae Park (Seoul National University); Songhwai Oh (Seoul National University)
37Learning Labeled Robot Affordance Models Using Simulations and CrowdsourcingAdam Allevato (UT Austin)*; Elaine Short (Tufts University); Mitch Pryor (UT Austin); Andrea Thomaz (UT Austin)
38Towards Embodied Scene DescriptionSinan Tan (Tsinghua University); Huaping Liu (Tsinghua University)*; Di Guo (Tsinghua University); Xinyu Zhang (Tsinghua University); Fuchun Sun (Tsinghua University)
39Reinforcement Learning based Control of Imitative Policies for Near-Accident DrivingZhangjie Cao (Stanford University); Erdem Biyik (Stanford University)*; Woodrow Wang (Stanford University); Allan Raventos (Toyota Research Institute); Adrien Gaidon (Toyota Research Institute); Guy Rosman (Toyota Research Institute); Dorsa Sadigh (Stanford)
40Deep Drone AcrobaticsElia Kaufmann (ETH / University of Zurich)*; Antonio Loquercio (ETH / University of Zurich); Rene Ranftl (Intel Labs); Matthias Müller (Intel Labs); Vladlen Koltun (Intel Labs); Davide Scaramuzza (University of Zurich & ETH Zurich, Switzerland)
41Active Preference-Based Gaussian Process Regression for Reward LearningErdem Biyik (Stanford University)*; Nicolas Huynh (École Polytechnique); Mykel Kochenderfer (Stanford University); Dorsa Sadigh (Stanford)
42A Bayesian Framework for Nash Equilibrium Inference in Human-Robot Parallel PlayShray Bansal (Georgia Institute of Technology)*; Jin Xu (Georgia Institute of Technology); Ayanna Howard (Georgia Institute of Technology); Charles Isbell (Georgia Institute of Technology)
43Data-driven modeling of a flapping bat robot with a single flexible wing surfaceJonathan Hoff (University of Illinois at Urbana-Champaign)*; Seth Hutchinson (Georgia Tech)
44Safe Motion Planning for Autonomous Driving using an Adversarial Road ModelAlex Liniger (ETH Zurich)*; Luc Van Gool (ETH Zurich)
45A Motion Taxonomy for Manipulation EmbeddingDavid Paulius (University of South Florida)*; Nicholas Eales (University of South Florida); Yu Sun (University of South Florida)
46Aerial Manipulation Using Hybrid Force and Position NMPC Applied to Aerial WritingDimos Tzoumanikas (Imperial College London)*; Felix Graule (ETH Zurich); Qingyue Yan (Imperial College London); Dhruv Shah (Berkeley Artificial Intelligence Research); Marija Popovic (Imperial College London); Stefan Leutenegger (Imperial College London)
47A Global Quasi-Dynamic Model for Contact-Trajectory Optimization in ManipulationBernardo Aceituno-Cabezas (MIT)*; Alberto Rodriguez (MIT)V
48Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of ObstaclesTravis Manderson (McGill University)*; Juan Camilo Gamboa Higuera (McGill University); Stefan Wapnick (McGill University); Jean-François Tremblay (McGill University); Florian Shkurti (University of Toronto); David Meger (McGill University); Gregory Dudek (McGill University)
49Spatio-Temporal Stochastic Optimization: Theory and Applications to Optimal Control and Co-DesignEthan Evans (Georgia Institute of Technology)*; Andrew Kendall (Georgia Institute of Technology); Georgios Boutselis (Georgia Institute of Technology ); Evangelos Theodorou (Georgia Institute of Technology)
50Kernel Taylor-Based Value Function Approximation for Continuous-State Markov Decision ProcessesJunhong Xu (INDIANA UNIVERSITY)*; Kai Yin (Vrbo, Expedia Group); Lantao Liu (Indiana University, Intelligent Systems Engineering)
51HMPO: Human Motion Prediction in Occluded Environments for Safe Motion PlanningJaesung Park (University of North Carolina at Chapel Hill)*; Dinesh Manocha (University of Maryland at College Park)
52Motion Planning for Variable Topology Truss Modular RobotChao Liu (University of Pennsylvania)*; Sencheng Yu (University of Pennsylvania); Mark Yim (University of Pennsylvania)
53Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement LearningArchit Sharma (Google)*; Michael Ahn (Google); Sergey Levine (Google); Vikash Kumar (Google); Karol Hausman (Google Brain); Shixiang Gu (Google Brain)
54Compositional Transfer in Hierarchical Reinforcement LearningMarkus Wulfmeier (DeepMind)*; Abbas Abdolmaleki (Google DeepMind); Roland Hafner (Google DeepMind); Jost Tobias Springenberg (DeepMind); Michael Neunert (Google DeepMind); Noah Siegel (DeepMind); Tim Hertweck (DeepMind); Thomas Lampe (DeepMind); Nicolas Heess (DeepMind); Martin Riedmiller (DeepMind)
55Learning from Interventions: Human-robot interaction as both explicit and implicit feedbackJonathan Spencer (Princeton University)*; Sanjiban Choudhury (University of Washington); Matt Barnes (University of Washington); Matthew Schmittle (University of Washington); Mung Chiang (Princeton University); Peter Ramadge (Princeton); Siddhartha Srinivasa (University of Washington)
56Fourier movement primitives: an approach for learning rhythmic robot skills from demonstrationsThibaut Kulak (Idiap Research Institute)*; Joao Silverio (Idiap Research Institute); Sylvain Calinon (Idiap Research Institute)
57Self-Supervised Localisation between Range Sensors and Overhead ImageryTim Tang (University of Oxford)*; Daniele De Martini (University of Oxford); Shangzhe Wu (University of Oxford); Paul Newman (University of Oxford)
58Probabilistic Swarm Guidance Subject to Graph Temporal Logic SpecificationsFranck Djeumou (University of Texas at Austin)*; Zhe Xu (University of Texas at Austin); Ufuk Topcu (University of Texas at Austin)
59In-Situ Learning from a Domain Expert for Real World Socially Assistive Robot DeploymentKatie Winkle (Bristol Robotics Laboratory)*; Severin Lemaignan (); Praminda Caleb-Solly (); Paul Bremner (); Ailie Turton (University of the West of England); Ute Leonards ()
60MRFMap: Online Probabilistic 3D Mapping using Forward Ray Sensor ModelsKumar Shaurya Shankar (Carnegie Mellon University)*; Nathan Michael (Carnegie Mellon University)
61GTI: Learning to Generalize across Long-Horizon Tasks from Human DemonstrationsAjay Mandlekar (Stanford University); Danfei Xu (Stanford University)*; Roberto Martín-Martín (Stanford University); Silvio Savarese (Stanford University); Li Fei-Fei (Stanford University)
62Agbots 2.0: Weeding Denser Fields with Fewer RobotsWyatt McAllister (University of Illinois)*; Joshua Whitman (University of Illinois); Allan Axelrod (University of Illinois); Joshua Varghese (University of Illinois); Girish Chowdhary (University of Illinois at Urbana Champaign); Adam Davis (University of Illinois)
63Optimally Guarding Perimeters and Regions with Mobile Range SensorsSiwei Feng (Rutgers University)*; Jingjin Yu (Rutgers Univ.)
64Learning Agile Robotic Locomotion Skills by Imitating AnimalsXue Bin Peng (UC Berkeley)*; Erwin Coumans (Google); Tingnan Zhang (Google); Tsang-Wei Lee (Google Brain); Jie Tan (Google); Sergey Levine (UC Berkeley)
65Learning to Manipulate Deformable Objects without DemonstrationsYilin Wu (UC Berkeley); Wilson Yan (UC Berkeley)*; Thanard Kurutach (UC Berkeley); Lerrel Pinto (); Pieter Abbeel (UC Berkeley)
66Deep Differentiable Grasp Planner for High-DOF GrippersMin Liu (National University of Defense Technology)*; Zherong Pan (University of North Carolina at Chapel Hill); Kai Xu (National University of Defense Technology); Kanishka Ganguly (University of Maryland at College Park); Dinesh Manocha (University of North Carolina at Chapel Hill)
67Ergodic Specifications for Flexible Swarm Control: From User Commands to Persistent AdaptationAhalya Prabhakar (Northwestern University)*; Ian Abraham (Northwestern University); Annalisa Taylor (Northwestern University); Millicent Schlafly (Northwestern University); Katarina Popovic (Northwestern University); Giovani Diniz (Raytheon); Brendan Teich (Raytheon); Borislava Simidchieva (Raytheon); Shane Clark (Raytheon); Todd Murphey (Northwestern Univ.)
68Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal ConstraintsShushman Choudhury (Stanford University)*; Jayesh Gupta (Stanford University); Mykel Kochenderfer (Stanford University); Dorsa Sadigh (Stanford); Jeannette Bohg (Stanford)
69Latent Belief Space Motion Planning under Cost, Dynamics, and Intent UncertaintyDicong Qiu (iSee); Yibiao Zhao (iSee); Chris Baker (iSee)*
70Learning of Sub-optimal Gait Controllers for Magnetic Walking Soft MillirobotsUtku Culha (Max-Planck Institute for Intelligent Systems); Sinan Ozgun Demir (Max Planck Institute for Intelligent Systems); Sebastian Trimpe (Max Planck Institute for Intelligent Systems); Metin Sitti (Carnegie Mellon University)*
71Nonparametric Motion Retargeting for Humanoid Robots on Shared Latent SpaceSungjoon Choi (Disney Research)*; Matthew Pan (Disney Research); Joohyung Kim (University of Illinois Urbana-Champaign)
72Residual Policy Learning for Shared AutonomyCharles Schaff (Toyota Technological Institute at Chicago)*; Matthew Walter (Toyota Technological Institute at Chicago)
73Efficient Parametric Multi-Fidelity Surface MappingAditya Dhawale (Carnegie Mellon University)*; Nathan Michael (Carnegie Mellon University)
74Towards neuromorphic control: A spiking neural network based PID controller for UAVRasmus Stagsted (University of Southern Denmark); Antonio Vitale (ETH Zurich); Jonas Binz (ETH Zurich); Alpha Renner (Institute of Neuroinformatics, University of Zurich and ETH Zurich); Leon Bonde Larsen (University of Southern Denmark); Yulia Sandamirskaya (Institute of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland)*
75Quantile QT-Opt for Risk-Aware Vision-Based Robotic GraspingCristian Bodnar (University of Cambridge)*; Adrian Li (X); Karol Hausman (Google Brain); Peter Pastor (X); Mrinal Kalakrishnan (X)
76Scaling data-driven robotics with reward sketching and batch reinforcement learningSerkan Cabi (DeepMind)*; Sergio Gómez Colmenarejo (DeepMind); Alexander Novikov (DeepMind); Ksenia Konyushova (DeepMind); Scott Reed (DeepMind); Rae Jeong (DeepMind); Konrad Zolna (DeepMind); Yusuf Aytar (DeepMind); David Budden (DeepMind); Mel Vecerik (Deepmind); Oleg Sushkov (DeepMind); David Barker (DeepMind); Jonathan Scholz (DeepMind); Misha Denil (DeepMind); Nando de Freitas (DeepMind); Ziyu Wang (Google Research, Brain Team)
77MPTC — Modular Passive Tracking Controller for stack of tasks based control frameworksJohannes Englsberger (German Aerospace Center (DLR))*; Alexander Dietrich (DLR); George Mesesan (German Aerospace Center (DLR)); Gianluca Garofalo (German Aerospace Center (DLR)); Christian Ott (DLR); Alin Albu-Schaeffer (Robotics and Mechatronics Center (RMC), German Aerospace Center (DLR))
78NH-TTC: A gradient-based framework for generalized anticipatory collision avoidanceBobby Davis (University of Minnesota Twin Cities)*; Ioannis Karamouzas (Clemson University); Stephen Guy (University of Minnesota Twin Cities)
793D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and HumansAntoni Rosinol (MIT)*; Arjun Gupta (MIT); Marcus Abate (MIT); Jingnan Shi (MIT); Luca Carlone (Massachusetts Institute of Technology)
80Robot Object Retrieval with Contextual Natural Language QueriesThao Nguyen (Brown University)*; Nakul Gopalan (Georgia Tech); Roma Patel (Brown University); Matthew Corsaro (Brown University); Ellie Pavlick (Brown University); Stefanie Tellex (Brown University)
81AlphaPilot: Autonomous Drone RacingPhilipp Foehn (ETH / University of Zurich)*; Dario Brescianini (University of Zurich); Elia Kaufmann (ETH / University of Zurich); Titus Cieslewski (University of Zurich & ETH Zurich); Mathias Gehrig (University of Zurich); Manasi Muglikar (University of Zurich); Davide Scaramuzza (University of Zurich & ETH Zurich, Switzerland)
82Concept2Robot: Learning Manipulation Concepts from Instructions and Human DemonstrationsLin Shao (Stanford University)*; Toki Migimatsu (Stanford University); Qiang Zhang (Shanghai Jiao Tong University); Kaiyuan Yang (Stanford University); Jeannette Bohg (Stanford)
83A Variable Rolling SLIP Model for a Conceptual Leg Shape to Increase Robustness of Uncertain Velocity on Unknown TerrainAdar Gaathon (Technion — Israel Institute of Technology)*; Amir Degani (Technion — Israel Institute of Technology)
84Interpreting and Predicting Tactile Signals via a Physics-Based and Data-Driven FrameworkYashraj Narang (NVIDIA)*; Karl Van Wyk (NVIDIA); Arsalan Mousavian (NVIDIA); Dieter Fox (NVIDIA)
85Learning Active Task-Oriented Exploration Policies for Bridging the Sim-to-Real GapJacky Liang (Carnegie Mellon University)*; Saumya Saxena (Carnegie Mellon University); Oliver Kroemer (Carnegie Mellon University)
86Manipulation with Shared GraspingYifan Hou (Carnegie Mellon University)*; Zhenzhong Jia (SUSTech); Matthew Mason (Carnegie Mellon University)
87Deep Learning Tubes for Tube MPCDavid Fan (Georgia Institute of Technology )*; Ali Agha (Jet Propulsion Laboratory); Evangelos Theodorou (Georgia Institute of Technology)
88Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier FunctionsJason Choi (UC Berkeley); Fernando Castañeda (UC Berkeley); Claire Tomlin (UC Berkeley); Koushil Sreenath (Berkeley)*
89Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent FuturesAllen Wang (MIT)*; Xin Huang (MIT); Ashkan Jasour (MIT); Brian Williams (Massachusetts Institute of Technology)
90Online Domain Adaptation for Occupancy MappingAnthony Tompkins (The University of Sydney)*; Ransalu Senanayake (Stanford University); Fabio Ramos (NVIDIA, The University of Sydney)
91ALGAMES: A Fast Solver for Constrained Dynamic GamesSimon Le Cleac’h (Stanford University)*; Mac Schwager (Stanford, USA); Zachary Manchester (Stanford)
92Scalable and Probabilistically Complete Planning for Robotic Spatial ExtrusionCaelan Garrett (MIT)*; Yijiang Huang (MIT Department of Architecture); Tomas Lozano-Perez (MIT); Caitlin Mueller (MIT Department of Architecture)
93The RUTH Gripper: Systematic Object-Invariant Prehensile In-Hand Manipulation via Reconfigurable UnderactuationQiujie Lu (Imperial College London)*; Nicholas Baron (Imperial College London); Angus Clark (Imperial College London); Nicolas Rojas (Imperial College London)
94Heterogeneous Graph Attention Networks for Scalable Multi-Robot Scheduling with Temporospatial ConstraintsZheyuan Wang (Georgia Institute of Technology)*; Matthew Gombolay (Georgia Institute of Technology)
95Robust Multiple-Path Orienteering Problem: Securing Against Adversarial AttacksGuangyao Shi (University of Maryland)*; Pratap Tokekar (University of Maryland); Lifeng Zhou (Virginia Tech)
96Eyes-Closed Safety Kernels: Safety of Autonomous Systems Under Loss of ObservabilityForrest Laine (UC Berkeley)*; Chih-Yuan Chiu (UC Berkeley); Claire Tomlin (UC Berkeley)
97Explaining Multi-stage Tasks by Learning Temporal Logic Formulas from Suboptimal DemonstrationsGlen Chou (University of Michigan)*; Necmiye Ozay (University of Michigan); Dmitry Berenson (U Michigan)
98Nonlinear Model Predictive Control of Robotic Systems with Control Lyapunov FunctionsRuben Grandia (ETH Zurich)*; Andrew Taylor (Caltech); Andrew Singletary (Caltech); Marco Hutter (ETHZ); Aaron Ames (Caltech)
99Learning to Slide Unknown Objects with Differentiable Physics SimulationsChangkyu Song (Rutgers University); Abdeslam Boularias (Rutgers University)*
100Reachable Sets for Safe, Real-Time Manipulator Trajectory DesignPatrick Holmes (University of Michigan); Shreyas Kousik (University of Michigan)*; Bohao Zhang (University of Michigan); Daphna Raz (University of Michigan); Corina Barbalata (Louisiana State University); Matthew Johnson Roberson (University of Michigan); Ram Vasudevan (University of Michigan)
101Learning Task-Driven Control Policies via Information BottlenecksVincent Pacelli (Princeton University)*; Anirudha Majumdar (Princeton)
102Simultaneously Learning Transferable Symbols and Language Groundings from Perceptual Data for Instruction FollowingNakul Gopalan (Georgia Tech)*; Eric Rosen (Brown University); Stefanie Tellex (Brown University); George Konidaris (Brown)
103A social robot mediator to foster collaboration and inclusion among childrenSarah Gillet (Royal Institute of Technology)*; Wouter van den Bos (University of Amsterdam); Iolanda Leite (KTH)
The RSS Foundation is the governing body behind the Robotics: Science and Systems (RSS) conference. The foundation was started and is run by volunteers from the robotics community who believe that an open, high-quality, single-track conference is an important component of an active and growing scientific discipline.
