257 citations · 1k across the 96 of their papers we have counts for
10 papers · 1 filter
Post-Training, Finetuning, and Steering:A Field Guide for Curating Training Data
Maria Attarian, Yifan Ruan, Claas Voelcker +5
While Vision-Language-Action models have enabled impressive cross-task transfer in zero-shot settings, \emph{adaptation} methods are often necessary to achieve consistent performan…
Foresight: Iterative Reasoning About Clues that Matter for Navigation
Arthur Zhang, Carl Qi, Donne Su +3
Open-world mapless navigation from sparse language instructions requires resolving underspecified goals and inferring which environmental cues are relevant for reaching the goal. F…
Self-Refining Vision Language Model for Robotic Failure Detection and Reasoning
Carl Qi, Xiaojie Wang, Silong Yong +6
Reasoning about failures is crucial for building reliable and trustworthy robotic systems. Prior approaches either treat failure reasoning as a closed-set classification problem or…
Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks
Viraj Joshi, Zifan Xu, Bo Liu +2
Multi-task Reinforcement Learning (MTRL) has emerged as a critical training paradigm for applying reinforcement learning (RL) to a set of complex real-world robotic tasks, which de…
CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance
Arthur Zhang, Harshit Sikchi, Amy Zhang +1
We introduce CREStE, a scalable learning-based mapless navigation framework to address the open-world generalization and robustness challenges of outdoor urban navigation. Key to a…
Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning
Caleb Chuck, Carl Qi, Michael J. Munje +13
Reinforcement Learning is a promising tool for learning complex policies even in fast-moving and object-interactive domains where human teleoperation or hard-coded policies might f…
Fairness-Sensitive Policy-Gradient Reinforcement Learning for Reducing Bias in Robotic Assistance
Jie Zhu, Mengsha Hu, Xueyao Liang +3
Robots assist humans in various activities, from daily living public service (e.g., airports and restaurants), and to collaborative manufacturing. However, it is risky to assume th…
LIV: Language-Image Representations and Rewards for Robotic Control
Yecheng Jason Ma, William Liang, Vaidehi Som +4
We present Language-Image Value learning (LIV), a unified objective for vision-language representation and reward learning from action-free videos with text annotations. Exploiting…
VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman +3
Reward and representation learning are two long-standing challenges for learning an expanding set of robot manipulation skills from sensory observations. Given the inherent cost an…
Intervention Design for Effective Sim2Real Transfer
Melissa Mozifian, Amy Zhang, Joelle Pineau +1
The goal of this work is to address the recent success of domain randomization and data augmentation for the sim2real setting. We explain this success through the lens of causal in…