Offline motion libraries and online MPC for advanced mobility skills

Abstract

We describe an optimization-based framework to perform complex locomotion skills for robots with legs and wheels. The generation of complex motions over a long-time horizon often requires offline computation due to current computing constraints and is mostly accomplished through trajectory optimization (TO). In contrast, model predictive control (MPC) focuses on the online computation of trajectories, robust even in the presence of uncertainty, albeit mostly over shorter time horizons and is prone to generating nonoptimal solutions over the horizon of the task’s goals. Our article’s contributions overcome this trade-off by combining offline motion libraries and online MPC, uniting a complex, long-time horizon plan with reactive, short-time horizon solutions. We start from offline trajectories that can be, for example, generated by TO or sampling-based methods. Also, multiple offline trajectories can be composed out of a motion library into a single maneuver. We then use these offline trajectories as the cost for the online MPC, allowing us to smoothly blend between multiple composed motions even in the presence of discontinuous transitions. The MPC optimizes from the measured state, resulting in feedback control, which robustifies the task’s execution by reacting to disturbances and looking ahead at the offline trajectory. With our contribution, motion designers can choose their favorite method to iterate over behavior designs offline without tuning robot experiments, enabling them to author new behaviors rapidly. Our experiments demonstrate complex and dynamic motions on our traditional quadrupedal robot ANYmal and its roller-walking version. Moreover, the article’s findings contribute to evaluating five planning algorithms.

Distributed Multi-robot Collaboration for Tasks Allocation based on Optimized Greedy Algorithm Approach

“…Other research focuses their efforts on the capacities that micro-robots must have in order to acquire cognitive capacities allowing them to evolve on the site to be studied and this by using reinforced learning methods [19]. Speaking of morphology, Cheetah 3 [20] from MIT or ANYmal from ANY-BOTICS, developed at ETH Zurich [21], are two concrete examples meeting DOF requirements in difficult sites with increased aggressiveness.…”

Versatile multicontact planning and control for legged loco-manipulation

“…However, when dealing with long-horizon dynamic maneuvers involving multiple discontinuous switches, the accumulated effects of unmodeled disturbances might render the plan unstabilizable with a purely reactive controller. Hence, we mitigated these issues with a two-layer tracking scheme (78,79) consisting of a short-horizon model predictive control (MPC) layer on top of a whole-body controller. The full architecture of the online module is depicted in Fig.…”

Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

“…Undoubtedly, accurately tuning the weights for the different cost terms is a tedious task [15] and having more physically meaningful references has been shown to be a preferable solution [16]. To address this problem, Bjelonic et al [17] use the result of an offline TO as cost terms for their MPC.…”

“…In this work, we propose a novel optimization-based reference generator layer that supplies a NMPC with references for CoM and Ground Reaction Forces (GRFs) that are suitable to the task of the robot. The novelty of our approach is that the reference trajectories are computed online (as opposed to the method used in [17]), solving a simplified optimization problem that takes into account the future robot behaviour, and the intermittent contact schedule to generate the references. It is worth highlighting the difference of our approach with the reference governors [18][19][20].…”