USC Researchers Unveil Breakthroughs in Robotics at ICRA 2024

| May 14, 2024

USC researchers present latest findings in robotics at premier global conference

Graphic featuring the ICRA conference logo and text stating ICRA 2024, MAY 13-17, YOKOHAMA, JAPAN

USC has a strong presence at ICRA 2024, a premier global venue for robotics research. Photo/ICRA.

Researchers from the USC School of Advanced Computing and the USC Viterbi School of Engineering are co-authors on 24 papers at the International Conference on Robotics and Automation (ICRA) in Japan this week, one of the premier gatherings in the field of robotics and automation.

Authors include faculty and students from the Thomas Lord Department of Computer Science, the Ming Hsieh Department of Computer Science and Electrical Engineering, and the Department of Aerospace and Mechanical Engineering exploring innovative work in multi-robot systems, imitation learning, preference-based reward learning, robotics with large language models, and more. In addition to the papers, USC researchers also served as chair or co-chair at multiple sessions.

For detailed program information, please see the ICRA conference website.

USC at ICRA 2024

PAPERS 

Safe Planning in Dynamic Environments Using Conformal Prediction

Lars Lindemann, Matthew Cleaveland, Gihyun Shim, George J. Pappas

Collision Avoidance and Navigation for a Quadrotor Swarm Using End-To-End Deep Reinforcement Learning

Zhehui Huang, Zhaojing Yang, Rahul Krupani, Baskın Şenbaşlar, Sumeet Batra, Gaurav S. Sukhatme

Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing

Bryon Tjanaka, Matthew C. Fontaine, David H. Lee, Aniruddha Kalkar, Stefanos Nikolaidis

AG-CVG: Coverage Planning with a Mobile Recharging UGV and an Energy-Constrained UAV

Nare Karapetyan, Ahmad Bilal Asghar, Amisha Bhaskar, Guangyao Shi, Dinesh Manocha, Pratap Tokekar

A Generalized Acquisition Function for Preference-Based Reward Learning

Evan Ellis, Gaurav R. Ghosal, Stuart J. Russell, Anca Dragan, Erdem Bıyık

SPRINT: Scalable Policy Pre-Training Via Language Instruction Relabeling

Jesse Zhang, Karl Pertsch, Jiahui Zhang, Joseph J. Lim

Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers

Aryaman Gupta, Kaustav Chakraborty, Somil Bansal

HyperPPO: A Scalable Method for Finding Small Policies for Robotic Control

Shashank Hegde, Zhehui Huang, Gaurav S. Sukhatme

 Conformal Predictive Safety Filter for RL Controllers in Dynamic Environments

Kegan J. Strawn, Nora Ayanian, Lars Lindemann

Conditionally Combining Robot Skills Using Large Language Models

K.R. Zentner, Ryan Julian, Brian Ichter, Gaurav S. Sukhatme

Benchmarking Multi-Robot Coordination in Realistic, Unstructured Human-Shared Environments

Lukas Heuer, Luigi Palmieri, Anna Mannucci, Sven Koenig

CppFlow: Generative Inverse Kinematics for Efficient and Robust Cartesian Path Planning

Jeremy Morgan, David Millard, Gaurav S. Sukhatme

Adaptation of Flipper-Mud Interactions Enables Effective Terrestrial Locomotion on Muddy Substrates

Shipeng Liu, Boyuan Huang, Feifei Qian

WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting

Kan Chen, Runzhou Ge, Hang Qiu, Rami AI-Rfou, Charles R. Qi, Xuanyu Zhou, Zoey Yang, Scott Ettinger, Pei Sun, Zhaoqi Leng, Mustafa Baniodeh, Ivan Bogun, Weiyue Wang, Mingxing Tan, Dragomir Anguelov

MPS: A New Method for Selecting the Stable Closed-Loop Equilibrium Attitude-Error Quaternion of a UAV During Flight

Francisco M. F. R. Gonçalves, Ryan M. Bena, Konstantin I. Matveev, Néstor O. Pérez-Arancibia

Optimization and Evaluation of a Multi-Robot Surface Inspection Task through Particle Swarm Optimization

Darren Chiu, Radhika Nagpal, Bahar Haghighat

Learning Agile Locomotion and Adaptive Behaviors via RL-augmented MPC

Yiyu Chen, Quan Nguyen

Improving Safety in Human-Robot Collaboration Via Mixed Reality-Augmented Deep Reinforcement Learning

Satyandra K. Gupta, Manyar, Omey Mohan

Demonstration of Dynamic Loco-Manipulation on HECTOR: Humanoid for Enhanced ConTrol and Open-Source Research

Junheng Li, Junchao Ma, Omar Kolt, Manas Shah, Quan Nguyen

Hierarchical Optimization-Based Control for Whole-Body Loco-Manipulation of Heavy Objects

Alberto Rigo, Muqun Hu, Satyandra K. Gupta, Quan Nguyen 

Using Large Language Models to Generate and Apply Contingency Handling Procedures in Collaborative Assembly Applications

Jeon Ho Kang, Neel Dhanaraj, Siddhant Ravindra Wadaskar, Satyandra K. Gupta

 Safety-Aware Perception for Autonomous Collision Avoidance in Dynamic Environments

Ryan Bena, Chongbo Zhao, Quan Nguyen

Multi-Robot Task Allocation under Uncertainty Via Hindsight Optimization

Neel Dhanaraj, Jeon Ho Kang, Anirban Mukherjee, Heramb Nemlekar, Stefanos Nikolaidis, Satyandra K. Gupta,

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Collaboration between 21 institutions including USC (Gaurav Sukhatme), led by Google DeepMind.

For detailed program information, please see the ICRA conference website.

ORAL SESSIONS 

Multi-Robot Systems I (oral session)

Chair: Gaurav Sukhatme (USC)

Co-Chair: Asako Kanezaki (Tokyo Institute of Technology)

Imitation Learning (oral session)

Chair: Edward Johns (Imperial College London)

Co-Chair: Erdem Bıyık (USC)

Robotics with Large Language Models (oral session)

Chair: Chiori Hori (Mitsubishi Electric Research Laboratories)

Co-Chair: Gaurav Sukhatme (USC)

Deep Learning III (oral session)

Chair: Gaurav Sukhatme (USC)

Co-Chair: John M. Dolan  (Carnegie Mellon University)

 

Published on May 14th, 2024

Last updated on May 16th, 2024

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