15 papers
RLE-Bench: A Qualifying Exam for Coding Agents as Robot Learning Engineers
Haitong Ma, Chenxiao Gao, Rushi Qiang +2
Coding agents are beginning to move beyond purely digital tasks to tackle physical-world challenges, particularly in robotics. Existing robotics benchmarks, however, primarily focu…
A Single Diffusion-Policy Controller for Multi-Task Block Pushing with Zero-Shot Sim-to-Real Transfer
Haitong Ma, Haldun Balim, Yang Hu +2
Diffusion policies have shown promising empirical performance in representing and learning complex maneuvers for robots using behavior cloning (BC). In this paper, we explore train…
Learning to Adapt: Representation-Based Reinforcement Learning for Multi-Task Skill Transfer
Aryan Naveen, Haitong Ma, Haldun Balim +1
Reinforcement learning has achieved remarkable success in learning complex control policies, yet its applicability remains limited due to sample inefficiency and poor generalizatio…
GeMPO: Generalized Measure Matching for Online Diffusion Reinforcement Learning
Haitong Ma, Chenxiao Gao, Tianyi Chen +2
A commonly used family of RL algorithms for diffusion policies conducts softmax reweighting over samples from the behavior policy, which often induces an overgreedy policy and fail…
A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents
Wei-Chieh Huang, Weizhi Zhang, Yueqing Liang +56
Research in artificial intelligence is shifting from model innovations and benchmark scores towards problem definition and rigorous real-world evaluation. As the field enters the "…
Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action Spaces
Haitong Ma, Ofir Nabati, Aviv Rosenberg +7
Reinforcement learning (RL) struggles to scale to large, combinatorial action spaces common in many real-world problems. This paper introduces a novel framework for training discre…
Offline Imitation Learning upon Arbitrary Demonstrations by Pre-Training Dynamics Representations
Haitong Ma, Bo Dai, Zhaolin Ren +2
Limited data has become a major bottleneck in scaling up offline imitation learning (IL). In this paper, we propose enhancing IL performance under limited expert data by introducin…
One-Step Flow Policy Mirror Descent
Tianyi Chen, Haitong Ma, Na Li +2
Diffusion policies have achieved great success in online reinforcement learning (RL) due to their strong expressive capacity. However, the inference of diffusion policy models reli…
Efficient Online Reinforcement Learning for Diffusion Policy
Haitong Ma, Tianyi Chen, Kai Wang +2
Diffusion policies have achieved superior performance in imitation learning and offline reinforcement learning (RL) due to their rich expressiveness. However, the conventional diff…
Distributed Thompson sampling under constrained communication
Saba Zerefa, Zhaolin Ren, Haitong Ma +1
In Bayesian optimization, a black-box function is maximized via the use of a surrogate model. We apply distributed Thompson sampling, using a Gaussian process as a surrogate model,…
Autonomous Robotic Ultrasound System for Liver Follow-up Diagnosis: Pilot Phantom Study
Tianpeng Zhang, Sekeun Kim, Jerome Charton +4
The paper introduces a novel autonomous robot ultrasound (US) system targeting liver follow-up scans for outpatients in local communities. Given a computed tomography (CT) image wi…
Efficient Duple Perturbation Robustness in Low-rank MDPs
Yang Hu, Haitong Ma, Bo Dai +1
The pursuit of robustness has recently been a popular topic in reinforcement learning (RL) research, yet the existing methods generally suffer from efficiency issues that obstruct…
Multi-Agent Coverage Control with Transient Behavior Consideration
Runyu Zhang, Haitong Ma, Na Li
This paper studies the multi-agent coverage control (MAC) problem where agents must dynamically learn an unknown density function while performing coverage tasks. Unlike many curre…
Skill Transfer and Discovery for Sim-to-Real Learning: A Representation-Based Viewpoint
Haitong Ma, Zhaolin Ren, Bo Dai +1
We study sim-to-real skill transfer and discovery in the context of robotics control using representation learning. We draw inspiration from spectral decomposition of Markov decisi…
Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding
Zhaolin Ren, Tongzheng Ren, Haitong Ma +2
This paper proposes an approach, Spectral Dynamics Embedding Control (SDEC), to optimal control for nonlinear stochastic systems. This method reveals an infinite-dimensional featur…