Aditya Mittal

Vision-language-action (VLA) models are pre-trained on large robot datasets and fine-tuned on hundreds of demonstrations per task, yet the natural way to teach a robot something new is to show it once. The prevailing answer for using that one demonstration is in-context learning, which retrains the policy to read demonstrations at inference. We ask whether something far simpler will do: keep the pre-trained VLA as it is and update it with a few gradient steps of its own training loss on the single demonstration. We call this Few-step One-Shot Imitation Learning (FOSIL). It works, and surprisingly well. One gradient step suffices when a familiar skill meets a new object or scene, ten for skills composed in a new order, and thirty for skills the policy could not perform before. We show this on a two-arm robot, with every result backed by 20 physical trials per task over 250 hours of evaluation, and on LIBERO-PRO, where the same demonstration given in context to the same backbone does not improve on the unadapted model. We then ask why so few steps are enough: robot pre-training decides the speed of adaptation, the useful update lives in a single direction per weight matrix, and a demonstration that moves differently from the policy can be refined with the policy’s own motion. A pretrained VLA, it turns out, is already a few gradient steps away from its next task. Physical robot rollouts and videos: https://fosilvla.github.io.

Behavior cloning for robot manipulation relies on expert demonstrations. However, for tasks that require dynamic stability, precise contact timing, or dexterous coordination, human operators may find it hard or even impossible to collect data. We study this infeasible-demonstration regime and propose GLIDE: Guardrails for Learning from Infeasible Demonstrations Efficiently, a framework that infers task-specific failure modes and converts them into executable guardrails for data collection and policy deployment. Given a task description and the conditioning teleoperation code, GLIDE writes guardrails that use system states to filter teleoperation and policy commands, constrain failure-prone actions, and iteratively improve from trajectory feedback. Across three tasks, GLIDE discovers emergent guardrails that go beyond domain-expert hardcoded ones, improving data collection over naive VR teleoperation and domain-expert hardcoded guardrails. After refinement, GLIDE raises data-collection success from 0–10% to 70–90% across the three tasks. During policy execution, mixed-data guarded policies reach 70%, 60%, and 60% success on Tomato plate transfer, Marker handover & stand, and Wine serving tasks. These results show that GLIDE can support policy learning when direct demonstrations are infeasible. Project website: http://guardrail-policy.github.io/.

Decision-making processes have increasingly come to rely on sophisticated machine learning tools, raising critical concerns about the fairness of their predictions with respect to sensitive groups. The widespread adoption of commercial "black-box" models necessitates careful consideration of their legal and ethical implications for consumers. When users interact with such black-box models, a key challenge arises: how can the influence of sensitive attributes, such as race or gender, be mitigated or removed from its predictions? We propose towerDebias (tDB), a novel post-processing method designed to reduce the influence of sensitive attributes in predictions made by black-box models. Our tDB approach leverages the Tower Property from probability theory to improve prediction fairness without requiring retraining of the original model. This method is highly versatile, as it requires no prior knowledge of the original algorithm's internal structure and is adaptable to a diverse range of applications. We present a formal fairness improvement theorem for tDB and showcase its effectiveness in both regression and classification tasks using multiple real-world datasets.

The growing influence of data science in statistics education requires tools that make key concepts accessible through real-world applications. We introduce "Data Science Looks At Discrimination" (dsld), an R package that provides a comprehensive set of analytical and graphical methods for examining issues of discrimination involving attributes such as race, gender, and age. By positioning fairness analysis as a teaching tool, the package enables instructors to demonstrate confounder effects, model bias, and related topics through applied examples. An accompanying 80-page Quarto book guides students and legal professionals in understanding these principles and applying them to real data. We describe the implementation of the package functions and illustrate their use with examples. Python interfaces are also available.