Learning Programmatically Structured Representations with Perceptor Gradients

We present the perceptor gradients algorithm -- a novel approach to learning symbolic representations based on the idea of decomposing an agent's policy into i) a perceptor network extracting symbols from raw observation data and ii) a task encoding program which maps the input symbols to output actions. We show that the proposed algorithm is able to learn representations that can be directly fed into a Linear-Quadratic Regulator (LQR) or a general purpose A* planner. Our experimental results confirm that the perceptor gradients algorithm is able to efficiently learn transferable symbolic representations as well as generate new observations according to a semantically meaningful specification.Comment: Published as a conference paper at ICLR 201

A Novel Design and Evaluation of a Dactylus-Equipped Quadruped Robot for Mobile Manipulation

Quadruped robots are usually equipped with additional arms for manipulation, negatively impacting price and weight. On the other hand, the requirements of legged locomotion mean that the legs of such robots often possess the needed torque and precision to perform manipulation. In this paper, we present a novel design for a small-scale quadruped robot equipped with two leg-mounted manipulators inspired by crustacean chelipeds and knuckle-walker forelimbs. By making use of the actuators already present in the legs, we can achieve manipulation using only 3 additional motors per limb. The design enables the use of small and inexpensive actuators relative to the leg motors, further reducing cost and weight. The moment of inertia impact on the leg is small thanks to an integrated cable/pulley system. As we show in a suite of tele-operation experiments, the robot is capable of performing single- and dual-limb manipulation, as well as transitioning between manipulation modes. The proposed design performs similarly to an additional arm while weighing and costing 5 times less per manipulator and enabling the completion of tasks requiring 2 manipulators.Comment: 6 pages, 10 figures, updated layout to fit in margins and corrected typos, accepted to the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022

Learning from Demonstration with Weakly Supervised Disentanglement

Robotic manipulation tasks, such as wiping with a soft sponge, require control from multiple rich sensory modalities. Human-robot interaction, aimed at teaching robots, is difficult in this setting as there is potential for mismatch between human and machine comprehension of the rich data streams. We treat the task of interpretable learning from demonstration as an optimisation problem over a probabilistic generative model. To account for the high-dimensionality of the data, a high-capacity neural network is chosen to represent the model. The latent variables in this model are explicitly aligned with high-level notions and concepts that are manifested in a set of demonstrations. We show that such alignment is best achieved through the use of labels from the end user, in an appropriately restricted vocabulary, in contrast to the conventional approach of the designer picking a prior over the latent variables. Our approach is evaluated in the context of two table-top robot manipulation tasks performed by a PR2 robot -- that of dabbing liquids with a sponge (forcefully pressing a sponge and moving it along a surface) and pouring between different containers. The robot provides visual information, arm joint positions and arm joint efforts. We have made videos of the tasks and data available - see supplementary materials at: https://sites.google.com/view/weak-label-lfd.Comment: 18 pages, 16 figures, accepted at the International Conference on Learning Representations (ICLR) 2021, supplementary website at https://sites.google.com/view/weak-label-lf