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 prospect to interview Jean Pierre Sleiman, writer of the paper “Versatile multicontact planning and management for legged loco-manipulation”, lately 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 component). Our examine particularly focused duties that might require a number of contact interactions to be solved, relatively than pick-and-place functions. 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 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 examine?
The analysis was pushed by the need to make such robots, particularly legged cellular 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. An ordinary strategy 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 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 facet of the door, go via the door whereas closing it, and so on.). Alternatively, a human skilled might reveal the 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 susceptible 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 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 all the loco-manipulation duties that we aimed to resolve might be modeled as Job 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 so on.), 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 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 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 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 so on.) 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 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 most important 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 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 offered framework as a stepping stone towards creating a completely autonomous loco-manipulation pipeline. Nevertheless, we see some limitations that we intention to deal with in future work. These limitations are primarily linked to the task-execution part, the place monitoring behaviors generated on the premise of pre-modeled environments is barely viable below the belief of a fairly correct description, which isn’t at all times easy to outline.

Robustness to modeling mismatches might be enormously improved by complementing our planner with data-driven strategies, resembling deep reinforcement studying (DRL). So one attention-grabbing course for future work could be to information the coaching of a strong DRL coverage utilizing dependable skilled 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 Programs Lab (RSL), ETH Zurich, Switzerland. His present analysis pursuits embrace optimization-based planning and management for legged cellular manipulation.

Daniel Carrillo-Zapata
was awared his PhD in swarm robotics on the Bristol Robotics Lab in 2020. He now fosters the tradition of “scientific agitation” to have interaction in two-way conversations between researchers and society.

Daniel Carrillo-Zapata
was awared his PhD in swarm robotics on the Bristol Robotics Lab in 2020. He now fosters the tradition of “scientific agitation” to have interaction in two-way conversations between researchers and society.


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