feat: SG/LP/Biased/Residual/GN/TD-MPPI (22~27번째 변형)#18
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Geonhee-LEE merged 2 commits intomainfrom Mar 20, 2026
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Denoising Score Matching으로 비용 지형의 score function을 학습하고, MPPI 가우시안 노이즈에 score 방향 bias를 추가하여 저비용 영역으로 유도. Score 미학습 시 순수 가우시안 fallback으로 graceful degradation 보장. - SGMPPIController: 단일/다중 반복 + DIAL 결합 + 온라인 학습 - ScoreNetwork: SigmaEmbedding + zero-init 출력층 - ScoreMatchingTrainer: 다중 σ 스케일 DSM Loss 학습 - 28 tests, 4-Way 벤치마크 (Vanilla vs DIAL vs Flow vs SG, 4 시나리오) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
LP-MPPI: Butterworth LPF 기반 주파수 도메인 smoothness (Kicki 2025) Biased-MPPI: 혼합 분포 샘플링 — J개 보조 정책 + (K-J)개 가우시안 (Trevisan 2024) Residual-MPPI: 사전 정책 nominal + MPPI 잔차 최적화 + KL 페널티 (Wang 2025) GN-MPPI: 가우스-뉴턴 2차 업데이트 + 병렬 라인 서치 + MPPI 폴백 (Homburger 2025) TD-MPPI: TD-learned terminal value V(x_T)로 짧은 호라이즌 장기 계획 (Crestaz 2026) - 5개 컨트롤러, 5개 벤치마크 (4-Way × 4 시나리오), 140개 테스트 추가 - AncillaryPolicy ABC + 5개 내장 정책 (PurePursuit/Braking/Feedback/MaxSpeed/PreviousSolution) - ValueNetwork + TDValueLearner + TDExperienceBuffer (온라인 TD 학습) - 전체 1519 tests 통과 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
6종 MPPI 변형 일괄 구현 (22~27번째):
주요 추가
Test plan
🤖 Generated with Claude Code