This paper presents a multi-modal model-based reinforcement learning (MBRL) approach to the excavation of fragmented rocks, which are very challenging to model due to their highly variable sizes and geometries, and visual occlusions. A multi-modal recurrent neural network (RNN) learns the dynamics of bucket-terrain interaction from a small physical dataset, with a discrete set of motion primitives encoded with domain knowledge as the action space. Then a model predictive controller (MPC) tracks a global reference path using multi-modal feedback. We show that our RNN-based dynamics function achieves lower prediction errors compared to a feed-forward neural network baseline, and the MPC is able to significantly outperform manually designed strategies on such a challenging task.

In this paper, we present an autonomous RGB-D and 2D ultrasound-guided robotic system for collecting 3D localized volumes of peripheral vessels. This compact design, with available commercial components, lends itself to platform utility throughout the human body. The fully integrated system works with force limits for future safety in human use. We propose a PID force controller for smooth and safe robot scanning following a priori 3D trajectory generated from a surface point cloud. System calibration is implemented to determine transformations among sensors, end-effector and robot base. A vascular localization pipeline that consists of detection and tracking is proposed to find the 3D vessel positions in real-time. Precision tests are performed with both predesignated and autonomously selected areas in an arm phantom. The average variance of the autonomously collected ultrasound images (to construct 3D volumes) between repeated tests is shown to be around 0.3 mm, similar to the theoretical spatial resolution a clinical ultrasound system. This fully integrated system demonstrates the capability of autonomous collection of peripheral vessels with built-in safety measures for future human testing.

We present a bilevel, contact-implicit trajectory optimization (TO) formulation that searches for robot trajectories with learned soft contact models. On the lower-level, contact forces are solved via a quadratic program (QP) with the maximum dissipation principle (MDP), based on which the dynamics constraints are formulated in the upper-level TO problem that uses direct transcription. Our method uses a contact model for granular media that is learned from physical experiments, but is general to any contact model that is stick-slip, convex, and smooth. We employ a primal interior-point method with a pre-specified duality gap to solve the lower-level problem, which provides robust gradient information to the upper-level problem. We evaluate our method by optimizing locomotion trajectories of a quadruped robot on various granular terrains offline, and show that we can obtain long-horizon walking gaits of high qualities.

This paper presents a semi-empirical method for simulating contact with elastically deformable objects whose force response is learned using entirely data-driven models. A point-based surface representation and an inhomogeneous, nonlinear force response model are learned from a robotic arm acquiring force-displacement curves from a small number of poking interactions. The simulator then estimates displacement and force response when the deformable object is in contact with an arbitrary rigid object. It does so by estimating displacements by solving a Hertzian contact model, and sums the expected forces at individual surface points through querying the learned point stiffness models as a function of their expected displacements. Experiments on a variety of challenging objects show that our approach learns force response with sufficient accuracy to generate plausible contact response for novel rigid objects.

In this paper, we propose a semi-empirical approach for simulating robot locomotion on granular media. We first develop a contact model based on the stick-slip behavior between rigid objects and granular grains, which is then learned through running extensive experiments. The contact model represents all possible contact wrenches that the granular substrate can provide as a convex volume, which our method formulates as constraints in an optimization-based contact force solver. During simulation, granular substrates are treated as rigid objects that allow penetration and the contact solver solves for wrenches that maximize frictional dissipation. We show that our method is able to simulate plausible interaction response with several granular media at interactive rates.