Learning Foresight without Explicit Trajectories for 3D Diffusion Policies
cs.RO, cs.CV
Submitted: 2026-09-17
Updated: 2026-09-17
Code: https://github.com/zhangzhongbo2213/movement-trend-guidance
Terminology
Sources
- 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations
- R3D: Revisiting 3D Policy Learning
- Causal World Modeling for Robot Control
- ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
- Behavior Generation with Latent Actions
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
- ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation
- Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation
- RVT-2: Learning Precise Manipulation from Few Demonstrations
- 3D Diffuser Actor: Policy Diffusion with 3D Scene Representations
- Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance
- Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulation
- Classifier-Free Diffusion Guidance
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
- DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects
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