1 citations · 2 across the 9 of their papers we have counts for
12 papers
Trajectory-Level Continuous Action Representation for Robotic Manipulation
Tong Yang, Jingkai Jia, Yuecheng Xu +3
We propose CAT, a trajectory-level continuous action representation framework for robotic manipulation. Existing visuomotor systems often entangle action representation with contro…
Representation-Aligned Tactile Grounding for Contact-Rich Robotic Manipulation
Ruilin Chen, Jingkai Jia, Tong Yang +8
Tactile-enhanced vision-language-action (VLA) policies have been introduced for contact-rich manipulation, where critical interaction states are often hidden from vision. Future ta…
EDAR: Learning Environment-Dependent Action Representations for Robotic Manipulation
Yuecheng Xu, Tong Yang, Jingkai Jia +3
Learning effective action representations is critical for robotic manipulation, where raw control trajectories are often noisy, redundant, and difficult to model directly. Existing…
SanD-Planner: Sample-Efficient Diffusion Planner in B-Spline Space for Robust Local Navigation
Jincheng Wang, Lingfan Bao, Tong Yang +3
The challenge of generating reliable local plans has long hindered practical applications in highly cluttered and dynamic environments. Key fundamental bottlenecks include acquirin…
Fast Visuomotor Policy for Robotic Manipulation
Jingkai Jia, Tong Yang, Xueyao Chen +2
We present a fast and effective policy framework for robotic manipulation, named Energy Policy, designed for high-frequency robotic tasks and resource-constrained systems. Unlike e…
Achieving Logarithmic Regret in KL-Regularized Zero-Sum Markov Games
Anupam Nayak, Tong Yang, Osman Yagan +2
Reverse Kullback-Leibler (KL) divergence-based regularization with respect to a fixed reference policy is widely used in modern reinforcement learning to preserve the desired trait…
Multi-head Transformers Provably Learn Symbolic Multi-step Reasoning via Gradient Descent
Tong Yang, Yu Huang, Yingbin Liang +1
Transformers have demonstrated remarkable capabilities in multi-step reasoning tasks. However, understandings of the underlying mechanisms by which they acquire these abilities thr…
Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games
Tong Yang, Bo Dai, Lin Xiao +1
Multi-agent reinforcement learning (MARL) lies at the heart of a plethora of applications involving the interaction of a group of agents in a shared unknown environment. A prominen…
Faster WIND: Accelerating Iterative Best-of- Distillation for LLM Alignment
Tong Yang, Jincheng Mei, Hanjun Dai +5
Recent advances in aligning large language models with human preferences have corroborated the growing importance of best-of-N distillation (BOND). However, the iterative BOND algo…
In-Context Learning with Representations: Contextual Generalization of Trained Transformers
Tong Yang, Yu Huang, Yingbin Liang +1
In-context learning (ICL) refers to a remarkable capability of pretrained large language models, which can learn a new task given a few examples during inference. However, theoreti…
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF
Shicong Cen, Jincheng Mei, Katayoon Goshvadi +6
Reinforcement learning from human feedback (RLHF) has demonstrated great promise in aligning large language models (LLMs) with human preference. Depending on the availability of pr…
Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning
Tong Yang, Shicong Cen, Yuting Wei +2
Federated reinforcement learning (RL) enables collaborative decision making of multiple distributed agents without sharing local data trajectories. In this work, we consider a mult…