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My research aims to enable robots to perform diverse and complex tasks in unstructured environments using deep learning. To achieve this, my lab and I develop scalable algorithms and systems for robot perception and control with the following focuses:
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Correspondence-Oriented Imitation Learning: Flexible Visuomotor Control with 3D Conditioning
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Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation
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Versatile Loco-Manipulation through Flexible Interlimb Coordination
Conference on Robot Learning (CoRL) 2025 (Oral Presentation) |
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Temporal Representation Alignment: Emergent Compositionality in Instruction Following with Successor Features
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Prompting with the Future: Open-World Model Predictive Control with Interactive Digital Twins
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Blox-Net: Generative Design-for-Robot-Assembly using VLM Supervision, Physics Simulation, and A Robot with Reset
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KALIE: Fine-Tuning Vision-Language Models for Open-World Manipulation without Robot Data
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Affordance-Guided Reinforcement Learning via Visual Prompting
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Should We Learn Contact-Rich Manipulation Policies from Sampling-Based Planners?
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Policy Adaptation via Language Optimization: Decomposing Tasks for Few-Shot Imitation
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Jacta: A Versatile Planner for Learning Dexterous and Whole-Body Manipulation
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MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting
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Open X-Embodiment: Robotic Learning Datasets and RT-X Models
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Stabilizing Contrastive RL: Techniques for Offline Goal Reaching
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Multi-Stage Cable Routing Through Hierarchical Imitation Learning
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Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control
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BridgeData V2: A Dataset for Robot Learning at Scale
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Active Task Randomization: Learning Robust Skills Via Unsupervised Generation of Diverse and Feasible Tasks
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Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks
Conference on Robot Learning (CoRL) 2022 (Oral Presentation) |
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Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space
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Discovering Generalizable Skills via Automated Generation of Diverse Tasks
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Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations
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Adaptive Procedural Task Generation for Hard-Exploration Problems
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KETO: Learning Keypoint Representations for Tool Manipulation
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Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation
Conference on Robot Learning (CoRL) 2019 (Oral Presentation) |
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Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks
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Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision
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Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
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Demo2Vec: Reasoning Object Affordances from Online Videos
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Recurrent Autoregressive Networks for Online Multi-Object Tracking
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DeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes
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