The ever-changing nature of human environments presents great challenges to robot manipulation. Objects that robots must manipulate vary in shape, wei

Toward next-generation learned robot manipulation

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2021-05-26 20:30:03

The ever-changing nature of human environments presents great challenges to robot manipulation. Objects that robots must manipulate vary in shape, weight, and configuration. Important properties of the robot, such as surface friction and motor torque constants, also vary over time. Before robot manipulators can work gracefully in homes and businesses, they must be adaptive to such variations. This survey summarizes types of variations that robots may encounter in human environments and categorizes, compares, and contrasts the ways in which learning has been applied to manipulation problems through the lens of adaptability. Promising avenues for future research are proposed at the end.

“Have we ever built a robot as capable as an ant, at any scale?” asked Mason (1) when talking about the variety and refinement of ant manipulation skills in his inspiring overview paper. The brain sizes of insects and animals are often much smaller than humans’, yet they can still demonstrate incredible manipulation skills. For example, octopuses can sense live crabs in plugged transparent jars and open them (2). They can also escape when trapped in containers closed with screw-on lids (3) or carry coconut halves with all tentacles while walking rapidly on the sea floor (4, 5).

As one might infer from the examples above, this review focuses on manipulation tasks that would be most naturally performed through contact between an agent (possibly a robot) and its environment. To be clear, we adopt Mason’s definition: “Manipulation refers to an agent’s control of its environment through selective contact.” (1) noting that “agent” refers to a human, animal, or robot. For example, the octopus contacts the jar and its lid, making use of its contacts to rotate the lid relative to the jar.

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