Interview with Jean Pierre Sleiman, writer 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, writer of the paper “Versatile multicontact planning and management for legged loco-manipulation”, not too long ago printed in Science Robotics.

What’s the subject of the analysis in your paper?
The analysis subject focuses on growing 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 component). Our research particularly focused duties that might require a number of contact interactions to be solved, reasonably than pick-and-place functions. To make sure our strategy just isn’t restricted to simulation environments, we utilized it to resolve real-world duties with a legged system consisting of the quadrupedal platform ANYmal geared up with DynaArm, a custom-built 6-DoF robotic arm.

May 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 will to make such robots, specifically legged cell manipulators, able to fixing a wide range of real-world duties, reminiscent of traversing doorways, opening/closing dishwashers, manipulating valves in an industrial setting, and so forth. A typical strategy would have been to deal with every job individually and independently by dedicating a considerable quantity of engineering effort to handcraft the specified behaviors:

That is usually achieved by means 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 ft, transfer the arm to the opposite aspect of the door, move by means of the door whereas closing it, and many others.). Alternatively, a human knowledgeable could reveal easy methods to clear up the duty by teleoperating the robotic, recording its movement, and having the robotic be taught to imitate the recorded conduct.

Nonetheless, this course of could be very gradual, tedious, and susceptible to engineering design errors. To keep away from this burden for each new job, 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 steerage for any of them.

May you clarify your methodology?
The important thing perception underlying our methodology was that the entire loco-manipulation duties that we aimed to resolve will be modeled as Process 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 plan a single bi-level optimization formulation that may embody all our duties, and exploit domain-specific data, reasonably 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 drawback.

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 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 many others.) and object affordances (these describe the place the robotic can work together with the item), a discrete state that captures the mix of all contact pairings is launched. Given a begin and aim 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 essential findings?
We discovered that our planning framework was in a position to quickly uncover complicated multi- contact plans for various loco-manipulation duties, regardless of having offered 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 growing a totally autonomous loco-manipulation pipeline. Nonetheless, we see some limitations that we purpose to deal with 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 beneath the idea of a fairly correct description, which isn’t at all times easy to outline.

Robustness to modeling mismatches will be significantly improved by complementing our planner with data-driven strategies, reminiscent of 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 writer

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 embrace optimization-based planning and management for legged cell manipulation.