—Robots often have to perform manipulation tasks in
close proximity to people (Fig 1). As such, it is desirable to use a
robot arm that has limited joint torques so as to not injure the
nearby person. Unfortunately, these limited torques then limit
the payload capability of the arm. By using contact with the
environment, robots can expand their reachable workspace that,
otherwise, would be inaccessible due to exceeding actuator torque
limits. We adapt our recently developed INSAT algorithm [1] to
tackle the problem of torque-limited whole arm manipulation
planning through contact. INSAT requires no prior over contact
mode sequence and no initial template or seed for trajectory
optimization. INSAT achieves this by interleaving graph search
to explore the manipulator joint configuration space with incre-
mental trajectory optimizations seeded by neighborhood solutions
to find a dynamically feasible trajectory through contact. We
demonstrate our results on a variety of manipulators and
scenarios in simulation. We also experimentally show our planner
exploiting robot-environment contact for the pick and place of a
payload using a Kinova Gen3 robot. In comparison to the same
trajectory running in free space, we experimentally show that the
utilization of bracing contacts reduces the overall torque required
to execute the trajectory.
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Efficient Humanoid Contact Planning using Learned Centroidal Dynamics Prediction
Humanoid robots dynamically navigate an environment by interacting with it via contact wrenches exerted at intermittent contact poses. Therefore, it is important to consider dynamics when planning a contact sequence. Traditional contact planning approaches assume a quasi-static balance criterion to reduce the computational challenges of selecting a contact sequence over a rough terrain. This however limits the applicability of the approach when dynamic motions are required, such as when walking down a steep slope or crossing a wide gap. Recent methods overcome this limitation with the help of efficient mixed integer convex programming solvers capable of synthesizing dynamic contact sequences. Nevertheless, its exponential-time complexity limits its applicability to short time horizon contact sequences within small environments. In this paper, we go beyond current approaches by learning a prediction of the dynamic evolution of the robot centroidal momenta, which can then be used for quickly generating dynamically robust contact sequences for robots with arms and legs using a search-based contact planner. We demonstrate the efficiency and quality of the results of the proposed approach in a set of dynamically challenging scenarios.
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- Award ID(s):
- 1825993
- NSF-PAR ID:
- 10109473
- Date Published:
- Journal Name:
- 2019 IEEE International Conference on Robotics and Automation (ICRA)
- Page Range / eLocation ID:
- 5280 to 5286
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICRA.2019.8794032
