FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space

Pretrained generative robot policies based on flow matching and diffusion have achieved impressive results across a wide range of manipulation tasks. Yet real-world deployments routinely expose failure modes outside the pretraining distribution. Closing these gaps typically requires large-scale data collection or online reinforcement learning on physical hardware, which is impractical for rapid and safe adaptation. We present FlowDAgger, a sample- and compute-efficient method for adapting frozen generative robot policies from human interventions in latent space. Our key idea is action inversion: each human expert action is mapped to the noise that would have produced it under the frozen base policy, using reverse-time integration followed by local refinement. The resulting inverted noise provides supervision for a lightweight latent policy that steers the base model at deployment time, enabling rapid skill acquisition while preserving its behavioral priors. We evaluate FlowDAgger in simulation and on real-world bimanual and single-arm manipulation, adapting both action-head VLAs and world-action models from a handful of interventions. FlowDAgger outperforms supervised fine-tuning and latent-space RL baselines and preserves pretrained skills on held-out tasks, offering a practical path for adapting robot foundation models in the real world. Website: https://microsoft.github.io/FlowDAgger

Benchmarking Affordance Generalization with BusyBox

Vision-Language-Action (VLA) models have been attracting the attention of researchers and practitioners thanks to their promise of generalization. Although single-task policies still offer competitive performance, VLAs are increasingly able to handle commands and environments unseen in their training set. While generalization in vision and language space is undoubtedly important for robust versatile behaviors, a key meta-skill VLAs need to possess is affordance generalization -- the ability to manipulate new objects with familiar physical features. In this work, we present BusyBox, a physical benchmark for systematic semi-automatic evaluation of VLAs' affordance generalization. BusyBox consists of 6 modules with switches, sliders, wires, buttons, a display, and a dial. The modules can be swapped and rotated to create a multitude of BusyBox variations with different visual appearances but the same set of affordances. We empirically demonstrate that generalization across BusyBox variants is highly challenging even for strong open-weights VLAs such as $π_{0.5}$ and GR00T-N1.6. To encourage the research community to evaluate their own VLAs on BusyBox and to propose new affordance generalization experiments, we have designed BusyBox to be easy to build in most robotics labs. We release the full set of CAD files for 3D-printing its parts as well as a bill of materials for (optionally) assembling its electronics. We also publish a dataset of language-annotated demonstrations that we collected using the common bimanual Mobile Aloha robot on the canonical BusyBox configuration. All of the released materials are available at https://microsoft.github.io/BusyBox.

LoLA: Long Horizon Latent Action Learning for General Robot Manipulation

The capability of performing long-horizon, language-guided robotic manipulation tasks critically relies on leveraging historical information and generating coherent action sequences. However, such capabilities are often overlooked by existing Vision-Language-Action (VLA) models. To solve this challenge, we propose LoLA (Long Horizon Latent Action Learning), a framework designed for robot manipulation that integrates long-term multi-view observations and robot proprioception to enable multi-step reasoning and action generation. We first employ Vision-Language Models to encode rich contextual features from historical sequences and multi-view observations. We further introduces a key module, State-Aware Latent Re-representation, which transforms visual inputs and language commands into actionable robot motion space. Unlike existing VLA approaches that merely concatenate robot proprioception (e.g., joint angles) with VL embeddings, this module leverages such robot states to explicitly ground VL representations in physical scale through a learnable "embodiment-anchored" latent space. We trained LoLA on diverse robotic pre-training datasets and conducted extensive evaluations on simulation benchmarks (SIMPLER and LIBERO), as well as two real-world tasks on Franka and Bi-Manual Aloha robots. Results show that LoLA significantly outperforms prior state-of-the-art methods (e.g., pi0), particularly in long-horizon manipulation tasks.