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”, just 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 sort out various loco-manipulation issues (i.e., manipulation issues inherently involving a locomotion factor). Our research particularly focused duties that might require a number of contact interactions to be solved, relatively than pick-and-place functions. To make sure our method shouldn’t be restricted to simulation environments, we utilized it to unravel real-world duties with a legged system consisting of the quadrupedal platform ANYmal geared up with DynaArm, a custom-built 6-DoF robotic arm.

Might you inform us concerning the implications of your analysis and why it’s an attention-grabbing space for research?
The analysis was pushed by the need to make such robots, specifically legged cell manipulators, able to fixing a wide range of real-world duties, resembling traversing doorways, opening/closing dishwashers, manipulating valves in an industrial setting, and so forth. A regular method would have been to sort out 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 during 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 ft, transfer the arm to the opposite facet of the door, go by the door whereas closing it, and many others.). Alternatively, a human professional might reveal find out how to clear up the duty by teleoperating the robotic, recording its movement, and having the robotic study to imitate the recorded habits.

Nevertheless, this course of could be very sluggish, tedious, and susceptible to engineering design errors. To keep away from this burden for each new process, the analysis opted for a extra structured method within the type of a single planner that may routinely 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 the entire loco-manipulation duties that we aimed to unravel could be modeled as Process 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 many others.), however nonetheless has to correctly combine them to unravel extra advanced 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 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 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 could 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 predominant 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 offered it with minimal steering. 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 could 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 totally 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 idea of pre-modeled environments is barely viable below the idea of a fairly correct description, which isn’t all the time easy to outline.

Robustness to modeling mismatches could be significantly improved by complementing our planner with data-driven strategies, resembling 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 professional demonstrations that may be quickly generated by our loco-manipulation planner to unravel 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 at the moment a Ph.D. candidate on the Robotic Techniques Lab (RSL), ETH Zurich, Switzerland. His present analysis pursuits embody optimization-based planning and management for legged cell manipulation.