We propose a two-phase method for learning safe control in fully controllable robotic manipulation tasks. In the first phase, the state-space support of supervisor demonstrations is estimated to infer implicit constraints. In the second phase, we present a switching policy to prevent the robot from leaving safe states. The policy switches between the robot’s learned policy and a novel failure-avoidance policy depending on the distance to the boundary of the support. We prove that inferred constraints are guaranteed to be enforced if the support is well-estimated. A simulated pushing task suggests that support estimation and failure avoidance control can reduce failures by 87% while sacrificing only 40% of performance. On a line tracking task using a da Vinci surgical robot, failure avoidance control reduced failures by 84%.
We propose and evaluate a two-phase calibration process for cable-driven robots. In Phase I, the robot performs a set of open-loop trajectories and learns a nonlinear transformation from camera pixels to joint values. In Phase II, the robot uses Phase I to move systematically to target points in a printed array, where a human operator manually adjusts the end-effector position. Phase I can reduce average error from 4.55mm to 2.14mm, and together with Phase II to 1.08mm. We apply this combination to clear away raisins and pumpkin seeds with success rate up to 99.2%, exceeding prior results and more than twice faster.
We explore how the Toyota HSR mobile manipulator robot can autonomously adjust the screw-cap on drip emitters for precision irrigation. As the built-in sensors do not provide sufficient accuracy for gripper alignment, we designed a lightweight, modular Emitter Localization Device (ELD) with cameras and LEDs that can be non-invasively mounted on the gripper. This paper presents details on the design, algorithms, and experiments in bringing the gripper into alignment with a sequence of 9 emitters using a two-phase procedure: 1) aligning the robot base using the built-in hand camera, and 2) aligning the gripper axis with the emitter axis using the ELD. Experiments suggest that each emitter can be reliably adjusted in under 20 seconds.
Discovery of Deep Continuous Options (DDCO) learns from demonstrations low-level continuous control skills parametrized by deep neural networks. A hybrid categorical–continuous distribution model parametrizes high-level policies that can invoke discrete options as well continuous control actions, and a cross-validation method tunes the number of options to be discovered. We evaluate DDCO in simulation of a 3-link robot, and in two physical experiments on the da Vinci surgical robot.
* Equal contribution
DART injects noise during supervisor demonstrations, with a noise level optimized to approximate the error of the robot’s trained policy. This forces the supervisor to demonstrate how to recover from errors, and doesn’t suffer from supervisor burden, computational cost, and training risk as much as on-policy methods. DART learns better policies faster and from fewer demonstrations.
We explore how characterizing supervisor inconsistency and correcting for this noise can improve task performance with a limited budget of data. In a planar part extraction task where human operators provide demonstrations by teleoperating a 2DOF robot, CNN models perform better when trained after error corrections.