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.
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.
We consider the problem of controlling a linear system with Gaussian noise and quadratic cost (LQG), using a memoryless controller that has limited capacity of the channel connecting its sensor to its actuator. We formulate this setting as a sequential rate-distortion (SRD) problem, where we minimize the rate of information required for the controller’s operation, under a constraint on its external cost. We present the optimality principle, and study the interesting and useful phenomenology of the optimal controller, such as the principled reduction of its order.
We consider the case where the controller is retentive (memory-utilizing). We can view the memory reader as one more sensor, and the memory writer as one more actuator. We can then formulate the problem of control under communication limitations, again as a sequential rate-distortion (SRD) problem of minimizing the rate of information required for the controller’s operation, under a constraint on its external cost. We show that this problem can be reduced to the memoryless case, studied in Part I. We then further investigate the form of the resulting optimal solution, and demonstrate its interesting phenomenology.
We formulate the problem of optimizing an agent under both extrinsic and intrinsic constraints on its operation in a dynamical system and develop the main tools for solving it. We identify the challenging convergence properties of the optimization algorithm, such as the bifurcation structure of the update operator near phase transitions. We study the special case of linear-Gaussian dynamics and quadratic cost (LQG), where the optimal solution has a particularly simple and solvable form. We also explore the learning task, where the model of the world dynamics is unknown and sample-based updates are used instead.