I am a research scientist and team lead of the Robotics Lab at Beijing Institute for General Artificial Intelligence. In the robotics lab, my colleagues and I hypothesize that there exists some fundamental representations or cognitive architectures underpinning the intelligent behaviors of humans. By uncovering and reproducing such an architecture, we hope to enable long-term human-robot shared autonomy by bridging
Perception (how to extract and organize more expressive symbols from dense sensory signals);
Reasoning (how to utilze thoes abstract information for higher-level skills, which in turn faciliates perception);
Task and Motion Planning (how to effectively act and react).
Before joining VCLA, I graduated with a B.S. in Mechanical Engineering and a B.S. in Computer Science with a Mathematics minor from Virginia Polytechnic Institute and State University (Virginia Tech) in 2016.
We extend our work in IJCV22 and ICRA21 by segmenting the objects in the panoptic map into parts and replace those parts by primitive shapes, which results in more realistic functionally equivalent scenes.
An attributed stochastic grammar is proposed to model the process of object fragmentation during cutting, which abstracts the spatial arrangement of fragments as node variables and captures the causality of cutting actions based on the fragmentation of parts.
We present a novel modular robot system capable of self-reconfiguration and achieving omnidirectional movements through magnetic docking for collaborative object transportation. Each robot in the system only equips a steerable omni wheel for navigation.
Instead of one-step aerial manipulation tasks, we investigate the sequential manipulation planning problem of UAMs, which requires coordinated motions of the vehicle’s floating base, the manipulator, and the object being manipulated over a long horizon.
To endow embodied AI agents with a deeper understanding of hand-object interactions, we design a data glove that can be reconfigured to collect grasping data in three modes: (i) force exerted by hand using piezoresistive material, (ii) contact points by grasping stably in VR, and (iii) reconstruct both visual and physical effects during the manipulation by integrating physics-based simulation.
We present an optimization framework to redesign an indoor scene by rearranging the furniture within it, which maximizes free space for service robots to operate while preserving human's preference for scene layout.
We rethink the problem of scene reconstruction from an embodied agent’s perspective. The objects within a reconstructed scene are segmented and replaced by part-based articulated CAD models to provide actionable information and afford finer-grained robot interactions.
We devise a 3D scene graph representation to abstract scene layouts with succinct geometric information and valid robot-scene interactions, such that a valid task plan can be computed using graph editing distance between the initial and the final scene graph while effectively satisfying constraints in motion level.
Leveraging the input redundancy in over-actuated UAVs, we tackle downwash effects between propellers with a novel control allocation framework that explores the entire allocation space for an optimal solution that reduces counteractions of airflows.
Learning key physical properties of tool-uses from a FEM-based simulation and enacting those properties via an optimal control-based motion planning scheme to produce tool-use strategies drastically different from observations, but with the least joint efforts.
A cooperative planning framework to generate optimal trajectories for a robot duo tethered by a flexible net to gather scattered objects spread in a large area. Implemented Model Reference Adaptive Control (MRAC) to handle unknown dynamics of carried payloads.
Given an interpretable And-Or-Graph knowledge representation, the proposed AR interface allows users to intuitively understand and supervise robot's behaviors, and interactively teach the robot with new actions.