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  • Zhao-Heng Yin
    Zhao-Heng Yin, Changhao Wang, Luis Pineda, Francois Hogan, Krishna Bodduluri, Akash Sharma, Patrick Lancaster, Ishita Prasad, Mrinal Kalakrishnan, Jitendra Malik, Mike Lambeta, Tingfan Wu, Pieter Abbeel, Mustafa Mukadam
  • DexGen - zhaohengyin. github. io
    Achieving Unprecedented Dexterity We introduce DexerityGen (DexGen), a foundational controller that empowers unprecedented dexterous manipulation skills, ranging from reorientation and assembly to the use of pen, screwdriver, and syringe
  • 3D Object Motion Field
    See Motion Learn Motion Motion is a fundamental element of control We present a learning framework that extracts object motion knowledge (representation) from human videos and uses it to teach robots to perform purposeful tasks—all without requiring robot demonstrations We use an image-shaped, dense 3D motion field to represent object movement Unlike sparse keypoints, it is informative
  • DexGen - zhaohengyin. github. io
    Retargeting as a Shape Correspondence Problem Human fingertips and robot fingertips have significantly different ranges of motion, as illustrated in the comparison below Retargeting presents a challenge in establishing a non-linear shape correspondence—specifically, how can the desired retargeting or correspondence be accurately defined?
  • Graph Search and Retrieval
    Our method is based on weighted behavior cloning on the high level: we put more weights to the good behavior in the suboptimal dataset We propose to use graph search and retrieval to implement this weighting procedure We first organize the demonstration into a graph using frozen or finetuned pretrained representations Then, we calculate the value of each transitions through a graph search
  • S GENERALIZATION OF V I LEARNING WITH -INVARIANT REGULARIZATION
    ABSTRACT How the visual imitation learning models can generalize to novel unseen visual observations is a highly challenging problem Such a generalization ability is very crucial for their real-world applications Since this generalization problem has many different aspects, we focus on one case called spatial generalization, which refers to generalization to unseen setup of object (entity
  • Planning for Sample Efficient Imitation Learning - GitHub Pages
    Abstract Imitation learning is a class of promising policy learning algorithms that is free from many practical issues with reinforcement learning, such as the reward design issue and the exploration hardness However, the current imitation algorithm strug-gles to achieve both high performance and high in-environment sample eficiency simultaneously Behavioral Cloning (BC) does not need in
  • Offline Imitation Learning Through Graph Search and Retrieval
    Zhao-Heng Yin and Pieter Abbeel UC Berkeley EECS Fig 1: Collecting high-quality human demonstrations for imitation learning can be very difficult Consider the problem of using a spoon, tying a rubber band, and picking up tiny objects with a tweezer These tasks require very precise and accurate manipulator movement A human operator can exhibit suboptimal behaviors and retry multiple times





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