Hanwen Ren

We introduce CAM-MCTS (Centralized Asynchronous Multi-Agent Monte Carlo Tree Search), a planning framework for collaborative object rearrangement in cluttered environments. CAM-MCTS enables multiple robots to coordinate manipulation actions while optimizing the overall task makespan. The method combines centralized planning with asynchronous execution so agents can begin new actions without waiting for global synchronization, reducing idle time and improving efficiency. The performance are verified in both simulation and real-robot systems.

We introduce an integrated framework that smartly combines multi-stage active sensing and MCTS-based retrieval planning for object retrieval in unknown, cluttered, confined environments. Our OR-MCTS planner relocates task-blocking objects while guiding sensing toward critical unseen regions. Experiments in simulation and real robots show improved success rate and efficiency over SOTA methods.

POM-NeTF is a physics-informed neural framework that enables robots to generate efficient object manipulation trajectories without expert demonstrations or trial-and-error learning. By directly solving the Eikonal equation through neural time fields and introducing a computationally efficient Dirichlet energy formulation, POM-NeTF learns manipulation skills that generalize across diverse objects and cluttered environments, supporting multimodal manipulation with adaptive regrasping.

We propose the first language-guided active sensing framework for confined, cluttered environments, enabling robots to perceive user-specified regions of interest through intelligent object manipulation. Our approach grounds natural language instructions in the environment, plans informative viewpoints, and iteratively rearranges view-blocking objects to achieve dense perception of otherwise occluded areas.

In this work, we propose a Multi-Stage Monte Carlo Tree Search (MS-MCTS) method leveraging an intelligent subgoal-focused tree expansion algorithm to find high-quality plans for complex non-monotone object rearrangement planning problems in confined environments. Our approach results in near-optimal solutions for various object rearrangement instances of diverse difficulty levels.

This paper presents a neural network-based object retrieval framework that efficiently performs rearrangement planning of unknown, arbitrary objects in confined spaces to retrieve the desired one. Our method demonstrates high performance by ensuring the relocation of non-target objects clear the way for the robot path homotopy to the given target object, thus significantly increasing the underlying motion planner's efficiency.

In this work, we present the active neural sensing approach that generates the kinematically feasible viewpoint sequences for the robot manipulator with an in-hand camera to gather the number of observations needed to reconstruct the underlying unknown cluttered environments. Our results exhibit high performance compared to traditional baselines regarding the number of viewpoints, scene coverage success rates, and planning time.

In this paper, we propose a novel, deep neural network-based method called CoGrasp that enables robots to grasp various objects in a human-aware manner by contextualizing human preference. Our user study indicates that our approach allows safe, natural, and social-aware human-robot co-grasping experience.