인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Imitation learning is widely used to learn robotic manipulation behaviors from expert demonstrations, but conventional behavior cloning primarily minimizes the discrepancy between demonstrated and predicted actions. As a result, task-oriented objectives and execution constraints may not be explicitly reflected in the learned policy, and small prediction errors may lead to task failure in states that are insufficiently covered by the demonstrations. This paper proposes Optimization-Based Imitation Learning (OB-IL), which combines a pretrained nominal policy, a lightweight residual prediction head, and residual quadratic programming (QP). The nominal ACT policy is initially trained from expert demonstrations and then frozen to preserve the learned task behavior. During policy rollouts, the residual QP computes a bounded corrective action that reduces a task residual while limiting deviations from the reference action. The QP-generated residual correction is used as supervision for the residual prediction head, which predicts corrections from the frozen ACT feature representation. During inference, the learned residual is combined with the ACT output and the resulting action is further refined using the residual QP. In MuJoCo peg-in-hole experiments, the nominal ACT policy achieved a 9.0% success rate with a median lateral alignment error of 6.43 mm, whereas OB-IL achieved an 89.0% success rate and reduced the median error to 0.85 mm. These results support the effectiveness of the proposed approach for the evaluated precision insertion task; extension to other manipulation tasks remains to be validated experimentally.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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