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Ordered Action Tokens for Visuomotor Policy Learning
Chaoqi Liu, Yue Zhao, Haonan Chen +4
Action tokenization maps continuous robot action chunks to discrete tokens and has become an important interface for modern visuomotor policies. Existing approaches either rely on…
B-spline Policy: Accelerating Manipulation Policies via B-spline Action Representations
Xiaoshen Han, Haoyu Xiong, Haonan Chen +4
In this work, we present B-spline Policy (BSP), an action representation designed for accelerating robot manipulation policies. Rather than predicting discrete-time action chunks,…
OAT: Ordered Action Tokenization
Chaoqi Liu, Xiaoshen Han, Jiawei Gao +3
Autoregressive policies offer a compelling foundation for scalable robot learning by enabling discrete abstraction, token-level reasoning, and flexible inference. However, applying…
Flexible Multitask Learning with Factorized Diffusion Policy
Chaoqi Liu, Haonan Chen, Sigmund H. Høeg +4
Multitask learning poses significant challenges due to the highly multimodal and diverse nature of robot action distributions. However, effectively fitting policies to these comple…
Multi-Modal Manipulation via Multi-Modal Policy Consensus
Haonan Chen, Jiaming Xu, Hongyu Chen +7
Effectively integrating diverse sensory modalities is crucial for robotic manipulation. However, the typical approach of feature concatenation is often suboptimal: dominant modalit…
Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning
Sigmund Hennum Høeg, Aksel Vaaler, Chaoqi Liu +2
Constructing robots to accomplish long-horizon tasks is a long-standing challenge within artificial intelligence. Approaches using generative methods, particularly Diffusion Models…
Localized Graph-Based Neural Dynamics Models for Terrain Manipulation
Chaoqi Liu, Yunzhu Li, Kris Hauser
Predictive models can be particularly helpful for robots to effectively manipulate terrains in construction sites and extraterrestrial surfaces. However, terrain state representati…
Learning Real-World Action-Video Dynamics with Heterogeneous Masked Autoregression
Lirui Wang, Kevin Zhao, Chaoqi Liu +1
We propose Heterogeneous Masked Autoregression (HMA) for modeling action-video dynamics to generate high-quality data and evaluation in scaling robot learning. Building interactive…
Implicit Graph Search for Planning on Graphs of Convex Sets
Ramkumar Natarajan, Chaoqi Liu, Howie Choset +1
Graphs of Convex Sets (GCS) is a recent method for synthesizing smooth trajectories by decomposing the planning space into convex sets, forming a graph to encode the adjacency rela…