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Jianlan LuoEmail: jianlanluo [at] eecs [dot] berkeley [dot] edu |
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I am a Postdoctoral Scholar at the Berkeley Artificial Intelligence Research (BAIR) Lab working with Prof. Sergey Levine.
Before moving back full-time to academia in 2022, I spent two years as a researcher at Google [X] working with Prof. Stefan Schaal.
I received my MS/Ph.D. from UC Berkeley in 2020.
I have also spent time at Deepmind, Everyday Robots.
Google Scholar | Twitter | GitHub |
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News
- We are organizing Workshop on Towards Reliable and Deployable Learning-Based Robotic Systems at CoRL 2023.
Research
My research vision centers on enabling robust long-term autonomy for complex real-world systems; drawing on tools and concepts from machine learning, controls, and robotics. Towards that end, my research agenda is focused on developing principled approaches for the design of high-performance, learning-based robotic systems, with the ultimate goal of deploying them successfully in complex, real-world environments.
Selected Publications [ Full List ]
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Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning
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Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation
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RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
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Yell At Your Robot: Improving On-the-Fly from Language Corrections
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Octo: An Open-Source Generalist Robot Policy
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SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning
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FMB: A Functional Manipulation Benchmark for Generalizable Robotic Learning
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RLIF: Interactive Imitation Learning as Reinforcement Learning
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Open X-Embodiment: Robotic Learning Datasets and RT-X Models
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Multi-Stage Cable Routing through Hierarchical Imitation Learning
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Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning
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Offline Meta-Reinforcement Learning for Industrial Insertion
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Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study
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Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly
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