Active Exploration for Robotic Manipulation

    •  Schneider, T., Belousov, B., Chalvatzaki, G., Romeres, D., Jha, D.K., Peters, J., "Active Exploration for Robotic Manipulation", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2022.
      BibTeX TR2022-139 PDF
      • @inproceedings{Schneider2022oct,
      • author = {Schneider, Tim and Belousov, Boris and Chalvatzaki, Georgia and Romeres, Diego and Jha, Devesh K. and Peters, Jan},
      • title = {Active Exploration for Robotic Manipulation},
      • booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      • year = 2022,
      • month = oct,
      • url = {}
      • }
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Robotic manipulation stands as a largely un- solved problem despite significant advances in robotics and machine learning in recent years. One of the key challenges in manipulation is the exploration of the dynamics of the environment when there is continuous contact between the objects being manipulated. This paper proposes a model-based active exploration approach that enables efficient learning in sparse-reward robotic manipulation tasks. The proposed method estimates an information gain objective using an ensemble of probabilistic models and deploys model predictive control (MPC) to plan actions online that maximize the expected reward while also performing directed exploration. We evaluate our proposed algorithm in simulation and on a real robot, trained from scratch with our method, on a challenging ball pushing task on tilted tables, where the target ball position is not known to the agent a-priori. Our real-world robot experiment serves as a fundamental application of active exploration in model-based reinforcement learning of complex robotic manipulation tasks.