no code implementations • 7 Dec 2024 • Ola Shorinwa, Zhiting Mei, Justin Lidard, Allen Z. Ren, Anirudha Majumdar
The remarkable performance of large language models (LLMs) in content generation, coding, and common-sense reasoning has spurred widespread integration into many facets of society.
1 code implementation • 4 Nov 2024 • Allen Z. Ren, Brian Ichter, Anirudha Majumdar
Large language models (LLMs) have exhibited remarkable reasoning and planning capabilities.
no code implementations • 2 Oct 2024 • Asher J. Hancock, Allen Z. Ren, Anirudha Majumdar
Vision-language-action (VLA) models trained on large-scale internet data and robot demonstrations have the potential to serve as generalist robot policies.
1 code implementation • 1 Sep 2024 • Allen Z. Ren, Justin Lidard, Lars L. Ankile, Anthony Simeonov, Pulkit Agrawal, Anirudha Majumdar, Benjamin Burchfiel, Hongkai Dai, Max Simchowitz
We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework including best practices for fine-tuning diffusion-based policies (e. g. Diffusion Policy) in continuous control and robot learning tasks using the policy gradient (PG) method from reinforcement learning (RL).
no code implementations • 23 Mar 2024 • Allen Z. Ren, Jaden Clark, Anushri Dixit, Masha Itkina, Anirudha Majumdar, Dorsa Sadigh
We consider the problem of Embodied Question Answering (EQA), which refers to settings where an embodied agent such as a robot needs to actively explore an environment to gather information until it is confident about the answer to a question.
no code implementations • 4 Jul 2023 • Allen Z. Ren, Anushri Dixit, Alexandra Bodrova, Sumeet Singh, Stephen Tu, Noah Brown, Peng Xu, Leila Takayama, Fei Xia, Jake Varley, Zhenjia Xu, Dorsa Sadigh, Andy Zeng, Anirudha Majumdar
Large language models (LLMs) exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remain prone to confidently hallucinated predictions.
no code implementations • 10 Apr 2023 • Zihan Ding, Yuanpei Chen, Allen Z. Ren, Shixiang Shane Gu, Qianxu Wang, Hao Dong, Chi Jin
Generating human-like behavior on robots is a great challenge especially in dexterous manipulation tasks with robotic hands.
no code implementations • 9 Feb 2023 • Allen Z. Ren, Hongkai Dai, Benjamin Burchfiel, Anirudha Majumdar
Addressing this issue, we propose AdaptSim, a new task-driven adaptation framework for sim-to-real transfer that aims to optimize task performance in target (real) environments -- instead of matching dynamics between simulation and reality.
no code implementations • 27 Jun 2022 • Allen Z. Ren, Bharat Govil, Tsung-Yen Yang, Karthik Narasimhan, Anirudha Majumdar
Robust and generalized tool manipulation requires an understanding of the properties and affordances of different tools.
no code implementations • 20 Jan 2022 • Kai-Chieh Hsu, Allen Z. Ren, Duy Phuong Nguyen, Anirudha Majumdar, Jaime F. Fisac
To improve safety, we apply a dual policy setup where a performance policy is trained using the cumulative task reward and a backup (safety) policy is trained by solving the Safety Bellman Equation based on Hamilton-Jacobi (HJ) reachability analysis.
no code implementations • 16 Nov 2021 • Abhinav Agarwal, Sushant Veer, Allen Z. Ren, Anirudha Majumdar
The key idea behind our approach is to utilize the generative model in order to implicitly specify a prior over policies.
1 code implementation • 13 Jul 2021 • Allen Z. Ren, Anirudha Majumdar
Our goal is to train control policies that generalize well to unseen environments.
2 code implementations • 5 Aug 2020 • Allen Z. Ren, Sushant Veer, Anirudha Majumdar
Control policies from imitation learning can often fail to generalize to novel environments due to imperfect demonstrations or the inability of imitation learning algorithms to accurately infer the expert's policies.