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 possibility to interview Jean Pierre Sleiman, creator of the paper “Versatile multicontact planning and management for legged loco-manipulation”, just lately revealed in Science Robotics.

What’s the matter of the analysis in your paper?
The analysis matter focuses on growing a model-based planning and management structure that allows legged cellular manipulators to deal with numerous loco-manipulation issues (i.e., manipulation issues inherently involving a locomotion ingredient). Our research particularly focused duties that might require a number of contact interactions to be solved, quite than pick-and-place purposes. To make sure our strategy isn’t restricted to simulation environments, we utilized it to unravel 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 fascinating space for research?
The analysis was pushed by the need to make such robots, specifically legged cellular manipulators, able to fixing a wide range of real-world duties, akin to traversing doorways, opening/closing dishwashers, manipulating valves in an industrial setting, and so forth. A normal strategy would have been to deal with every activity individually and independently by dedicating a considerable quantity of engineering effort to handcraft the specified behaviors:

That is usually achieved by way of using 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, cross by way of the door whereas closing it, and so on.). Alternatively, a human skilled might display the best way to clear up the duty by teleoperating the robotic, recording its movement, and having the robotic be taught to imitate the recorded habits.

Nevertheless, this course of could be very gradual, tedious, and vulnerable 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 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 the entire loco-manipulation duties that we aimed to unravel may be modeled as Job and Movement Planning (TAMP) issues. TAMP is a well-established framework that has been primarily used to unravel 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 so on.), however nonetheless has to correctly combine them to unravel extra complicated 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 data, quite 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 in a position to obtain an efficient search technique that solves the optimization downside.

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 may 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 article), a discrete state that captures the mix of all contact pairings is launched. Given a begin and objective 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 by way of 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 in a position to quickly uncover complicated multi- contact plans for numerous 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 may be reliably executed with an actual legged cellular manipulator.

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

Robustness to modeling mismatches may be tremendously improved by complementing our planner with data-driven methods, akin to deep reinforcement studying (DRL). So one fascinating path for future work could be to information the coaching of a sturdy DRL coverage utilizing dependable skilled demonstrations that may be quickly generated by our loco-manipulation planner to unravel a set of difficult duties with minimal reward-engineering.

In regards to the creator

Jean-Pierre Sleiman acquired 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 embody optimization-based planning and management for legged cellular manipulation.