Efficient Test-Time Adaptation For Robot Policies
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2605. 03065v2 Announce Type: replace Abstract: Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning.
arXiv:2605. 12236v2 Announce Type: replace-cross Abstract: Fine-tuning pre-trained robot policies with reinforcement learning (RL) often inherits the bottlenecks introduced by pre-training with behavioral cloning (BC), which produces narrow action distributions that lack the coverage necessary for downstream exploration.
arXiv:2609.37591v1 Announce Type: cross Abstract: Test-time adaptation for vision-language navigation (TTA-VLN) enables pretrained policies to adapt online to unseen environments using only test-time...
arXiv:2608. 11363v1 Announce Type: cross Abstract: A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction.
The paper introduces Keyframe Mnemonics, a self‑supervised approach that identifies critical observations (mnemonics) to aid behavior cloning in non‑Markovian settings. By training a policy conditioned on these keyframes, the method achieves perfect success rates on synthetic memory tasks and improves performance on a real‑robot manipulation benchmark, maintaining high success even at horizons far beyond training. The approach offers theoretical context‑retention guarantees and practical scalability to long‑horizon decision making.
arXiv:2610.17709v1 Announce Type: cross Abstract: Imitation learning typically treats every demonstration state as equally important, optimizing a behavior-cloning objective uniformly across the data...