Kavli Affiliate: Xiang Zhang
| First 5 Authors: Xiang Zhang, Masayoshi Tomizuka, Hui Li, ,
| Summary:
Contact-rich manipulation tasks often exhibit a large sim-to-real gap. For
instance, industrial assembly tasks frequently involve tight insertions where
the clearance is less than (0.1) mm and can even be negative when dealing
with a deformable receptacle. This narrow clearance leads to complex contact
dynamics that are difficult to model accurately in simulation, making it
challenging to transfer simulation-learned policies to real-world robots. In
this paper, we propose a novel framework for robustly learning manipulation
skills for real-world tasks using only the simulated data. Our framework
consists of two main components: the “Force Planner” and the “Gain Tuner”.
The Force Planner is responsible for planning both the robot motion and desired
contact forces, while the Gain Tuner dynamically adjusts the compliance control
gains to accurately track the desired contact forces during task execution. The
key insight of this work is that by adaptively adjusting the robot’s compliance
control gains during task execution, we can modulate contact forces in the new
environment, thereby generating trajectories similar to those trained in
simulation and narrows the sim-to-real gap. Experimental results show that our
method, trained in simulation on a generic square peg-and-hole task, can
generalize to a variety of real-world insertion tasks involving narrow or even
negative clearances, all without requiring any fine-tuning.
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