Workshop Of Aligning Robot Representations With Humans We suggest that because humans are the ultimate evaluator of robot performance, we must explicitly focus our efforts on aligning learned representations with humans, in addition to learning the downstream task. We demonstrate how human aligned representations can lead to novel human behavior models with broad implications beyond robotics, to econometrics and cognitive science.
Workshop Of Aligning Robot Representations With Humans We suggest that because humans are the ultimate evaluator of robot performance, we must explicitly focus our efforts on aligning learned representations with humans, in addition to learning the downstream task. We propose that because humans will be the ultimate evaluator of task performance in the world, it is crucial that we explicitly focus our efforts on aligning robot representations with humans, in addition to learning the downstream task. To act in the world, robots rely on a representation of salient task aspects: for example, to carry a coffee mug, a robot may consider movement efficiency or mu. Our key insight is that effective learning from human input requires first explicitly learning good intermediate representations and then using those representations for solving downstream.
Aligning Robot Representations With Humans To act in the world, robots rely on a representation of salient task aspects: for example, to carry a coffee mug, a robot may consider movement efficiency or mu. Our key insight is that effective learning from human input requires first explicitly learning good intermediate representations and then using those representations for solving downstream. In this workshop, we are interested in exploring ways in which robots can align their representations with those of the humans they interact with so that they can more effectively learn from their input. We demonstrate how human aligned representations can lead to novel human behavior models with broad implications beyond robotics, to econometrics and cognitive science. We suggest that because humans are the ultimate evaluator of robot performance, we must explicitly focus our eforts on aligning learned representations with humans, in addition to learning the downstream task. This work delineates the misalignment problem in human and robot observations, then proposes a novel framework in which robots infer human intentions and reason about human utilities through interaction, paving the way for more personalized and adaptive assistive robotic systems.
Aligning Robot And Human Representations Electrical And Computer In this workshop, we are interested in exploring ways in which robots can align their representations with those of the humans they interact with so that they can more effectively learn from their input. We demonstrate how human aligned representations can lead to novel human behavior models with broad implications beyond robotics, to econometrics and cognitive science. We suggest that because humans are the ultimate evaluator of robot performance, we must explicitly focus our eforts on aligning learned representations with humans, in addition to learning the downstream task. This work delineates the misalignment problem in human and robot observations, then proposes a novel framework in which robots infer human intentions and reason about human utilities through interaction, paving the way for more personalized and adaptive assistive robotic systems.
Aligning Robot And Human Representations Deepai We suggest that because humans are the ultimate evaluator of robot performance, we must explicitly focus our eforts on aligning learned representations with humans, in addition to learning the downstream task. This work delineates the misalignment problem in human and robot observations, then proposes a novel framework in which robots infer human intentions and reason about human utilities through interaction, paving the way for more personalized and adaptive assistive robotic systems.
Pdf Aligning Robot Representations With Humans
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