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”, lately printed 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 allows legged cell manipulators to deal with various loco-manipulation issues (i.e., manipulation issues inherently involving a locomotion aspect). Our examine particularly focused duties that may require a number of contact interactions to be solved, somewhat than pick-and-place purposes. To make sure our strategy will not 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 in regards to the implications of your analysis and why it’s an attention-grabbing space for examine?
The analysis was pushed by the need to make such robots, specifically legged cell manipulators, able to fixing quite a lot of real-world duties, similar to traversing doorways, opening/closing dishwashers, manipulating valves in an industrial setting, and so forth. An ordinary strategy would have been to deal with every process individually and independently by dedicating a considerable quantity of engineering effort to handcraft the specified behaviors:

That is sometimes achieved by the usage of hard-coded state-machines through which 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 facet of the door, move by the door whereas closing it, and so on.). Alternatively, a human knowledgeable might reveal how you can clear up the duty by teleoperating the robotic, recording its movement, and having the robotic study to imitate the recorded habits.

Nonetheless, this course of may be very gradual, tedious, and vulnerable to engineering design errors. To keep away from this burden for each new process, the analysis opted for a extra structured strategy within the type of a single planner that may routinely uncover the mandatory behaviors for a variety of loco-manipulation duties, with out requiring any detailed steerage 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 will 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., choose object, place object, transfer to object, throw object, and so on.), however nonetheless has to correctly combine them to resolve extra advanced long-horizon duties.

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

The principle technical novelty in our work lies within the Offline Multi-Contact Planning Module, depicted in Module B of Determine 1 within the paper. Its total setup will be summarized as follows: Ranging from a user-defined set of robotic end-effectors (e.g., entrance left foot, entrance proper foot, gripper, and so on.) and object affordances (these describe the place the robotic can work together with the item), 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 drawback 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 major findings?
We discovered that our planning framework was in a position to quickly uncover advanced multi- contact plans for various loco-manipulation duties, regardless of having supplied it with minimal steerage. For instance, for the door-traversal situation, 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 will be reliably executed with an actual legged cell manipulator.

What additional work are you planning on this space?
We see the offered framework as a stepping stone towards creating a completely autonomous loco-manipulation pipeline. Nonetheless, we see some limitations that we purpose to handle in future work. These limitations are primarily linked to the task-execution part, the place monitoring behaviors generated on the idea of pre-modeled environments is just viable below the belief of a fairly correct description, which isn’t all the time easy to outline.

Robustness to modeling mismatches will be significantly improved by complementing our planner with data-driven strategies, similar to deep reinforcement studying (DRL). So one attention-grabbing course for future work can be to information the coaching of a sturdy DRL coverage utilizing dependable knowledgeable demonstrations that may be quickly generated by our loco-manipulation planner to resolve a set of difficult duties with minimal reward-engineering.

Concerning 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 presently a Ph.D. candidate on the Robotic Methods Lab (RSL), ETH Zurich, Switzerland. His present analysis pursuits embrace optimization-based planning and management for legged cell manipulation.