Events

PhD Defense Efficiently Manipulating Clutter via Learning and Search-Based Reasoning
Export Event Save iCal Send to Google Calendar Send to Yahoo Calendar Send to Outlook Live Send to Microsoft Outlook Save iCal Preserve formatting in description (only supported in some calendar applications) Close Tuesday, May 06, 2025, 04:00pm - 06:00pm

Speaker: Baichuan Huang

Bio

Location : Room 402, 4th floor, 1 Spring Street, Downtown New Brunswick

Committee: 

Associate Professor Jingjin Yu 

Associate Professor Abdeslam Boularias

Professor Kostas Bekris

Bowen Wen (external)

Event Type: PhD Defense

Abstract: Object rearrangement is a crucial and complex problem in robotic manipulation, with applications in warehouse automation, household assistance, and industrial manufacturing. This dissertation presents novel approaches to enhance the efficiency and robustness of object manipulation planning in dynamic and cluttered environments. We introduce the Deep Interaction Prediction Network (DIPN), a learning-based model that accurately predicts object interactions, achieving over 90% accuracy in motion estimation. By integrating DIPN with Monte Carlo Tree Search (MCTS), we enable effective planning of non-prehensile actions, leading to a 100% success rate in challenging retrieval tasks. To further accelerate planning, we propose the Parallel Monte Carlo Tree Search with Batched Simulations (PMBS) framework, leveraging GPU-accelerated physics simulations to achieve a 30× speed-up. Experimental results in both simulation and real-world settings validate our approach, demonstrating state-of-the-art performance in success rates, solution quality, and computational efficiency, advancing robotic autonomy in unstructured environments.

Organization: 

Contact  Associate Professor Jingjin Yu

Zoom links: https://rutgers.zoom.us/j/91419464801?pwd=2tSAnrFpnVjCN91X03ML1N8EK7XBpj.1