This work aims at leveraging instructional video to guide the solving of complex multi-contact task-and-motion planning tasks in robotics. Towards this goal, we propose an extension of the well-established Rapidly-Exploring Random Tree (RRT) planner, which simultaneously grows multiple trees around grasp and release states extracted from the guiding video. Our key novelty lies in combining contact states, and 3D object poses extracted from the guiding video with a traditional planning algorithm that allows us to solve tasks with sequential dependencies, for example, if an object needs to be placed at a specific location to be grasped later. To demonstrate the benefits of the proposed video-guided planning approach, we design a new benchmark with three challenging tasks: (i) 3D re-arrangement of multiple objects between a table and a shelf, (ii) multi-contact transfer of an object through a tunnel, and (iii) transferring objects using a tray in a similar way a waiter transfers dishes. We demonstrate the effectiveness of our planning algorithm on several robots, including the Franka Emika Panda and the KUKA KMR iiwa.

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A seamless integration of robots into human environments requires robots to learn how to use existing human tools. Current approaches for learning tool manipulation skills mostly rely on expert demonstrations provided in the target robot environment, for example, by manually guiding the robot manipulator or by teleoperation. In this work, we introduce an automated approach that replaces an expert demonstration with a Youtube video for learning a tool manipulation strategy. The main contributions are twofold. First, we design an alignment procedure that aligns the simulated environment with the realworld scene observed in the video. This is formulated as an optimization problem that finds a spatial alignment of the tool trajectory to maximize the sparse goal reward given by the environment. Second, we describe an imitation learning approach that focuses on the trajectory of the tool rather than the motion of the human. For this we combine reinforcement learning with an optimization procedure to find a control policy and the placement of the robot based on the tool motion in the aligned environment. We demonstrate the proposed approach on spade, scythe and hammer tools in simulation, and show the effectiveness of the trained policy for the spade on a real Franka Emika Panda robot demonstration.