95 citations · 378 across the 58 of their papers we have counts for
11 papers · 1 filter
NaVILA: Legged Robot Vision-Language-Action Model for Navigation
An-Chieh Cheng, Yandong Ji, Zhaojing Yang +7
This paper proposes to solve the problem of Vision-and-Language Navigation with legged robots, which not only provides a flexible way for humans to command but also allows the robo…
Accurate and Data-Efficient Toxicity Prediction when Annotators Disagree
Harbani Jaggi, Kashyap Murali, Eve Fleisig +1
When annotators disagree, predicting the labels given by individual annotators can capture nuances overlooked by traditional label aggregation. We introduce three approaches to pre…
Trajectory Improvement and Reward Learning from Comparative Language Feedback
Zhaojing Yang, Miru Jun, Jeremy Tien +3
Learning from human feedback has gained traction in fields like robotics and natural language processing in recent years. While prior works mostly rely on human feedback in the for…
Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functions
Ayush Jain, Norio Kosaka, Xinhu Li +3
In reinforcement learning, off-policy actor-critic methods like DDPG and TD3 use deterministic policy gradients: the Q-function is learned from environment data, while the actor ma…
EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data
Jesse Zhang, Minho Heo, Zuxin Liu +4
Most reinforcement learning (RL) methods focus on learning optimal policies over low-level action spaces. While these methods can perform well in their training environments, they…
Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation
Michelle Pan, Mariah Schrum, Vivek Myers +2
Adaptive brain stimulation can treat neurological conditions such as Parkinson's disease and post-stroke motor deficits by influencing abnormal neural activity. Because of patient…
A Generalized Acquisition Function for Preference-based Reward Learning
Evan Ellis, Gaurav R. Ghosal, Stuart J. Russell +2
Preference-based reward learning is a popular technique for teaching robots and autonomous systems how a human user wants them to perform a task. Previous works have shown that act…
ViSaRL: Visual Reinforcement Learning Guided by Human Saliency
Anthony Liang, Jesse Thomason, Erdem Bıyık
Training robots to perform complex control tasks from high-dimensional pixel input using reinforcement learning (RL) is sample-inefficient, because image observations are comprised…
Batch Active Learning of Reward Functions from Human Preferences
Erdem Bıyık, Nima Anari, Dorsa Sadigh
Data generation and labeling are often expensive in robot learning. Preference-based learning is a concept that enables reliable labeling by querying users with preference question…
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning
Anthony Liang, Guy Tennenholtz, Chih-wei Hsu +3
We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates. We model episod…
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback
Yufei Wang, Zhanyi Sun, Jesse Zhang +4
Reward engineering has long been a challenge in Reinforcement Learning (RL) research, as it often requires extensive human effort and iterative processes of trial-and-error to desi…