USC at RSS 2024

| July 16, 2024 

Topics explored by USC researchers at this year’s Robotics: Science and Systems (RSS) conference include innovative perspectives on manipulation, locomotion, and control.

A graphic with an illustration of a robotic arm and the text July 15-19, 2024. USC at RSS 2024. Location: Delft, Netherlands

The 20th edition of the “Robotics: Science and Systems” (RSS) conference to be held at the Delft University of Technology, Delft, Netherlands in July 15-19, 2024.

 

This year’s Robotics: Science and Systems (RSS) conference, held in Delft, Netherlands, will showcase USC research highlighting the latest breakthroughs in robotics, from evaluation for robotic manipulation and solutions for robotic assembly problems to safety and performance guarantees.  

RSS is an annual conference that focuses on the intersection of robotics and science, covering a broad range of topics including robot design, perception, planning, control, and more. It has a strong reputation for showcasing cutting-edge research and attracting top researchers in the robotics community. 


Accepted papers with USC affiliation: 

Manipulation 

THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation 

Wilbert Pumacay, Ishika Singh, Jiafei Duan, Ranjay Krishna, Jesse Thomason, Dieter Fox 

Abstract: To realize effective large-scale, real-world robotic applications, we must evaluate how well our robot policies adapt to changes in environmental conditions. Unfortunately, a majority of studies evaluate robot performance in environments closely resembling or even identical to the training setup. We present THE COLOSSEUM, a novel simulation benchmark, with 20 diverse manipulation tasks, that enables systematical evaluation of models across 14 axes of environmental perturbations. These perturbations include changes in color, texture, and size of objects, table-tops, and backgrounds; we also vary lighting, distractors, physical properties perturbations and camera pose. Using THE COLOSSEUM, we compare 5 state-of-the-art manipulation models to reveal that their success rate degrades between 30-50% across these perturbation factors. When multiple perturbations are applied in unison, the success rate degrades ≥75%. We identify that changing the number of distractor objects, target object color, or lighting conditions are the perturbations that reduce model performance the most. To verify the ecological validity of our results, we show that our results in simulation are correlated ( ̄R2 = 0.614) to similar perturbations in real-world experiments. We open source code for others to use THE COLOSSEUM, and also release code to 3D print the objects used to replicate the real-world perturbations. Ultimately, we hope that THE COLOSSEUM will serve as a benchmark to identify modeling decisions that systematically improve generalization for manipulation. 

Locomotion and manipulation 

AutoMate: Specialist and Generalist Assembly Policies over Diverse Geometries 

Bingjie Tang, Iretiayo Akinola, Jie Xu, Bowen Wen, Ankur Handa, Karl Van Wyk, Dieter Fox, Gaurav S. Sukhatme, Fabio Ramos, Yashraj Narang 

Abstract: Robotic assembly for high-mixture settings requires adaptivity to diverse parts and poses, which is an open challenge. Meanwhile, in other areas of robotics, large models and sim-to-real have led to tremendous progress. Inspired by such work, we present AutoMate, a learning framework and system that consists of 4 parts: 1) a dataset of 100 assemblies compatible with simulation and the real world, along with parallelized simulation environments for policy learning, 2) a novel simulation-based approach for learning specialist (i.e., part-specific) policies and generalist (i.e., unified) assembly policies, 3) demonstrations of specialist policies that individually solve 80 assemblies with ≈80%+ success rates in simulation, as well as a generalist policy that jointly solves 20 assemblies with an 80%+ success rate, and 4) zero-shot sim-to-real transfer that achieves similar (or better) performance than simulation, including on perception-initialized assembly. The key methodological takeaway is that a union of diverse algorithms from manufacturing engineering, character animation, and time-series analysis provides a generic and robust solution for a diverse range of robotic assembly problems. To our knowledge, AutoMate provides the first simulation-based framework for learning specialist and generalist policies over a wide range of assemblies, as well as the first system demonstrating zero-shot sim-to-real transfer over such a range. 

Control  

Hamilton-Jacobi Reachability Analysis for Hybrid Systems with Controlled and Forced Transitions  

Javier Borquez, Shuang Peng, Yiyu Chen, Quan Nguyen, Somil Bansal 

Abstract: Hybrid dynamical systems with nonlinear dynamics are one of the most general modeling tools for representing robotic systems, especially contact-rich systems. However, providing guarantees regarding the safety or performance of nonlinear hybrid systems remains a challenging problem because it requires simultaneous reasoning about continuous state evolution and discrete mode switching. In this work, we address this problem by extending classical Hamilton-Jacobi (HJ) reachability analysis, a formal verification method for continuous-time nonlinear dynamical systems, to hybrid dynamical systems. We characterize the reachable sets for hybrid systems through a generalized value function defined over discrete and continuous states of the hybrid system. We also provide a numerical algorithm to compute this value function and obtain the reachable set. Our framework can compute reachable sets for hybrid systems consisting of multiple discrete modes, each with its own set of nonlinear continuous dynamics, discrete transitions that can be directly commanded or forced by a discrete control input, while still accounting for control bounds and adversarial disturbances in the state evolution. Along with the reachable set, the proposed framework also provides an optimal continuous and discrete controller to ensure system safety. We demonstrate our framework in several simulation case studies, as well as on a real-world testbed to solve the optimal mode planning problem for a quadruped with multiple gaits. 

Published on July 16th, 2024

Last updated on July 16th, 2024

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