Interview with Jean Pierre Sleiman, creator of the paper “Versatile multicontact planning and management for legged loco-manipulation”

Image from paper “Versatile multicontact planning and management for legged loco-manipulation“. © American Affiliation for the Development of Science

We had the prospect to interview Jean Pierre Sleiman, creator of the paper “Versatile multicontact planning and management for legged loco-manipulation”, not too long ago revealed in Science Robotics.

What’s the subject of the analysis in your paper?
The analysis subject focuses on creating a model-based planning and management structure that permits legged cellular manipulators to sort out various loco-manipulation issues (i.e., manipulation issues inherently involving a locomotion ingredient). Our examine particularly focused duties that might require a number of contact interactions to be solved, relatively than pick-and-place purposes. To make sure our strategy shouldn’t be restricted to simulation environments, we utilized it to resolve real-world duties with a legged system consisting of the quadrupedal platform ANYmal outfitted with DynaArm, a custom-built 6-DoF robotic arm.

Might you inform us concerning the implications of your analysis and why it’s an fascinating space for examine?
The analysis was pushed by the will to make such robots, particularly legged cellular manipulators, able to fixing quite a lot of real-world duties, resembling traversing doorways, opening/closing dishwashers, manipulating valves in an industrial setting, and so forth. An ordinary strategy would have been to sort out every activity individually and independently by dedicating a considerable quantity of engineering effort to handcraft the specified behaviors:

That is usually achieved via using hard-coded state-machines wherein the designer specifies a sequence of sub-goals (e.g., grasp the door deal with, open the door to a desired angle, maintain the door with one of many toes, transfer the arm to the opposite aspect of the door, go via the door whereas closing it, and many others.). Alternatively, a human professional could reveal the best way to remedy the duty by teleoperating the robotic, recording its movement, and having the robotic be taught to imitate the recorded conduct.

Nevertheless, this course of could be very gradual, tedious, and susceptible to engineering design errors. To keep away from this burden for each new activity, the analysis opted for a extra structured strategy within the type of a single planner that may robotically uncover the required behaviors for a variety of loco-manipulation duties, with out requiring any detailed steering for any of them.

Might you clarify your methodology?
The important thing perception underlying our methodology was that all the loco-manipulation duties that we aimed to resolve might be modeled as Activity and Movement Planning (TAMP) issues. TAMP is a well-established framework that has been primarily used to resolve sequential manipulation issues the place the robotic already possesses a set of primitive expertise (e.g., decide object, place object, transfer to object, throw object, and many others.), however nonetheless has to correctly combine them to resolve extra complicated long-horizon duties.

This attitude enabled us to plot a single bi-level optimization formulation that may embody all our duties, and exploit domain-specific data, relatively than task-specific data. By combining this with the well-established strengths of various planning methods (trajectory optimization, knowledgeable graph search, and sampling-based planning), we have been capable of obtain an efficient search technique that solves the optimization downside.

The primary technical novelty in our work lies within the Offline Multi-Contact Planning Module, depicted in Module B of Determine 1 within the paper. Its general setup might be summarized as follows: Ranging from a user-defined set of robotic end-effectors (e.g., entrance left foot, entrance proper foot, gripper, and many others.) and object affordances (these describe the place the robotic can work together with the thing), a discrete state that captures the mixture of all contact pairings is launched. Given a begin and purpose state (e.g., the robotic ought to find yourself behind the door), the multi-contact planner then solves a single-query downside by incrementally rising a tree through a bi-level search over possible contact modes collectively with steady robot-object trajectories. The ensuing plan is enhanced with a single long-horizon trajectory optimization over the found contact sequence.

What have been your important findings?
We discovered that our planning framework was capable of quickly uncover complicated multi- contact plans for various loco-manipulation duties, regardless of having offered it with minimal steering. For instance, for the door-traversal state of affairs, we specify the door affordances (i.e., the deal with, again floor, and entrance floor), and solely present a sparse goal by merely asking the robotic to finish up behind the door. Moreover, we discovered that the generated behaviors are bodily constant and might be reliably executed with an actual legged cellular manipulator.

What additional work are you planning on this space?
We see the introduced framework as a stepping stone towards creating a totally autonomous loco-manipulation pipeline. Nevertheless, we see some limitations that we intention to handle in future work. These limitations are primarily related to the task-execution section, the place monitoring behaviors generated on the premise of pre-modeled environments is simply viable beneath the belief of a fairly correct description, which isn’t all the time easy to outline.

Robustness to modeling mismatches might be significantly improved by complementing our planner with data-driven methods, resembling deep reinforcement studying (DRL). So one fascinating course for future work could be to information the coaching of a strong DRL coverage utilizing dependable professional demonstrations that may be quickly generated by our loco-manipulation planner to resolve a set of difficult duties with minimal reward-engineering.

In regards to the creator

Jean-Pierre Sleiman obtained the B.E. diploma in mechanical engineering from the American College of Beirut (AUB), Lebanon, in 2016, and the M.S. diploma in automation and management from Politecnico Di Milano, Italy, in 2018. He’s at the moment a Ph.D. candidate on the Robotic Methods Lab (RSL), ETH Zurich, Switzerland. His present analysis pursuits embody optimization-based planning and management for legged cellular manipulation.