A Modular Haptic Display with Reconfigurable Signals for Personalized Information Transfer

We present a customizable soft haptic system that integrates modular hardware with an information-theoretic algorithm to personalize feedback for different users and tasks. Our platform features modular, multi-degree-of-freedom pneumatic displays, where different signal types, such as pressure, frequency, and contact area, can be activated or combined using fluidic logic circuits. These circuits simplify control by reducing reliance on specialized electronics and enabling coordinated actuation of multiple haptic elements through a compact set of inputs. Our approach allows rapid reconfiguration of haptic signal rendering through hardware-level logic switching without rewriting code. Personalization of the haptic interface is achieved through the combination of modular hardware and software-driven signal selection. To determine which display configurations will be most effective, we model haptic communication as a signal transmission problem, where an agent must convey latent information to the user. We formulate the optimization problem to identify the haptic hardware setup that maximizes the information transfer between the intended message and the user's interpretation, accounting for individual differences in sensitivity, preferences, and perceptual salience. We evaluate this framework through user studies where participants interact with reconfigurable displays under different signal combinations. Our findings support the role of modularity and personalization in creating multimodal haptic interfaces and advance the development of reconfigurable systems that adapt with users in dynamic human-machine interaction contexts.

L2D2: Robot Learning from 2D Drawings

Robots should learn new tasks from humans. But how do humans convey what they want the robot to do? Existing methods largely rely on humans physically guiding the robot arm throughout their intended task. Unfortunately -- as we scale up the amount of data -- physical guidance becomes prohibitively burdensome. Not only do humans need to operate robot hardware but also modify the environment (e.g., moving and resetting objects) to provide multiple task examples. In this work we propose L2D2, a sketching interface and imitation learning algorithm where humans can provide demonstrations by drawing the task. L2D2 starts with a single image of the robot arm and its workspace. Using a tablet, users draw and label trajectories on this image to illustrate how the robot should act. To collect new and diverse demonstrations, we no longer need the human to physically reset the workspace; instead, L2D2 leverages vision-language segmentation to autonomously vary object locations and generate synthetic images for the human to draw upon. We recognize that drawing trajectories is not as information-rich as physically demonstrating the task. Drawings are 2-dimensional and do not capture how the robot's actions affect its environment. To address these fundamental challenges the next stage of L2D2 grounds the human's static, 2D drawings in our dynamic, 3D world by leveraging a small set of physical demonstrations. Our experiments and user study suggest that L2D2 enables humans to provide more demonstrations with less time and effort than traditional approaches, and users prefer drawings over physical manipulation. When compared to other drawing-based approaches, we find that L2D2 learns more performant robot policies, requires a smaller dataset, and can generalize to longer-horizon tasks. See our project website: https://collab.me.vt.edu/L2D2/

Counterfactual Behavior Cloning: Offline Imitation Learning from Imperfect Human Demonstrations

Learning from humans is challenging because people are imperfect teachers. When everyday humans show the robot a new task they want it to perform, humans inevitably make errors (e.g., inputting noisy actions) and provide suboptimal examples (e.g., overshooting the goal). Existing methods learn by mimicking the exact behaviors the human teacher provides -- but this approach is fundamentally limited because the demonstrations themselves are imperfect. In this work we advance offline imitation learning by enabling robots to extrapolate what the human teacher meant, instead of only considering what the human actually showed. We achieve this by hypothesizing that all of the human's demonstrations are trying to convey a single, consistent policy, while the noise and sub-optimality within their behaviors obfuscates the data and introduces unintentional complexity. To recover the underlying policy and learn what the human teacher meant, we introduce Counter-BC, a generalized version of behavior cloning. Counter-BC expands the given dataset to include actions close to behaviors the human demonstrated (i.e., counterfactual actions that the human teacher could have intended, but did not actually show). During training Counter-BC autonomously modifies the human's demonstrations within this expanded region to reach a simple and consistent policy that explains the underlying trends in the human's dataset. Theoretically, we prove that Counter-BC can extract the desired policy from imperfect data, multiple users, and teachers of varying skill levels. Empirically, we compare Counter-BC to state-of-the-art alternatives in simulated and real-world settings with noisy demonstrations, standardized datasets, and real human teachers. See videos of our work here: https://youtu.be/XaeOZWhTt68

CIVIL: Causal and Intuitive Visual Imitation Learning

Today's robots learn new tasks by imitating human examples. However, this standard approach to visual imitation learning is fundamentally limited: the robot observes what the human does, but not why the human chooses those behaviors. Without understanding the features that factor into the human's decisions, robot learners often misinterpret the data and fail to perform the task when the environment changes. We therefore propose a shift in perspective: instead of asking human teachers just to show what actions the robot should take, we also enable humans to indicate task-relevant features using markers and language prompts. Our proposed algorithm, CIVIL, leverages this augmented data to filter the robot's visual observations and extract a feature representation that causally informs human actions. CIVIL then applies these causal features to train a transformer-based policy that emulates human behaviors without being confused by visual distractors. Our simulations, real-world experiments, and user study demonstrate that robots trained with CIVIL can learn from fewer human demonstrations and perform better than state-of-the-art baselines, especially in previously unseen scenarios. See videos at our project website: https://civil2025.github.io

Safe Interaction via Monte Carlo Linear-Quadratic Games

Safety is critical during human-robot interaction. But -- because people are inherently unpredictable -- it is often difficult for robots to plan safe behaviors. Instead of relying on our ability to anticipate humans, here we identify robot policies that are robust to unexpected human decisions. We achieve this by formulating human-robot interaction as a zero-sum game, where (in the worst case) the human's actions directly conflict with the robot's objective. Solving for the Nash Equilibrium of this game provides robot policies that maximize safety and performance across a wide range of human actions. Existing approaches attempt to find these optimal policies by leveraging Hamilton-Jacobi analysis (which is intractable) or linear-quadratic approximations (which are inexact). By contrast, in this work we propose a computationally efficient and theoretically justified method that converges towards the Nash Equilibrium policy. Our approach (which we call MCLQ) leverages linear-quadratic games to obtain an initial guess at safe robot behavior, and then iteratively refines that guess with a Monte Carlo search. Not only does MCLQ provide real-time safety adjustments, but it also enables the designer to tune how conservative the robot is -- preventing the system from focusing on unrealistic human behaviors. Our simulations and user study suggest that this approach advances safety in terms of both computation time and expected performance. See videos of our experiments here: https://youtu.be/KJuHeiWVuWY.

A Unified Framework for Robots that Influence Humans over Long-Term Interaction

Robot actions influence the decisions of nearby humans. Here influence refers to intentional change: robots influence humans when they shift the human's behavior in a way that helps the robot complete its task. Imagine an autonomous car trying to merge; by proactively nudging into the human's lane, the robot causes human drivers to yield and provide space. Influence is often necessary for seamless interaction. However, if influence is left unregulated and uncontrolled, robots will negatively impact the humans around them. Prior works have begun to address this problem by creating a variety of control algorithms that seek to influence humans. Although these methods are effective in the short-term, they fail to maintain influence over time as the human adapts to the robot's behaviors. In this paper we therefore present an optimization framework that enables robots to purposely regulate their influence over humans across both short-term and long-term interactions. Here the robot maintains its influence by reasoning over a dynamic human model which captures how the robot's current choices will impact the human's future behavior. Our resulting framework serves to unify current approaches: we demonstrate that state-of-the-art methods are simplifications of our underlying formalism. Our framework also provides a principled way to generate influential policies: in the best case the robot exactly solves our framework to find optimal, influential behavior. But when solving this optimization problem becomes impractical, designers can introduce their own simplifications to reach tractable approximations. We experimentally compare our unified framework to state-of-the-art baselines and ablations, and demonstrate across simulations and user studies that this framework is able to successfully influence humans over repeated interactions. See videos of our experiments here: https://youtu.be/nPekTUfUEbo

Kiri-Spoon: A Kirigami Utensil for Robot-Assisted Feeding

For millions of adults with mobility limitations, eating meals is a daily challenge. A variety of robotic systems have been developed to address this societal need. Unfortunately, end-user adoption of robot-assisted feeding is limited, in part because existing devices are unable to seamlessly grasp, manipulate, and feed diverse foods. Recent works seek to address this issue by creating new algorithms for food acquisition and bite transfer. In parallel to these algorithmic developments, however, we hypothesize that mechanical intelligence will make it fundamentally easier for robot arms to feed humans. We therefore propose Kiri-Spoon, a soft utensil specifically designed for robot-assisted feeding. Kiri-Spoon consists of a spoon-shaped kirigami structure: when actuated, the kirigami sheet deforms into a bowl of increasing curvature. Robot arms equipped with Kiri-Spoon can leverage the kirigami structure to wrap-around morsels during acquisition, contain those items as the robot moves, and then compliantly release the food into the user's mouth. Overall, Kiri-Spoon combines the familiar and comfortable shape of a standard spoon with the increased capabilities of soft robotic grippers. In what follows, we first apply a stakeholder-driven design process to ensure that Kiri-Spoon meets the needs of caregivers and users with physical disabilities. We next characterize the dynamics of Kiri-Spoon, and derive a mechanics model to relate actuation force to the spoon's shape. The paper concludes with three separate experiments that evaluate (a) the mechanical advantage provided by Kiri-Spoon, (b) the ways users with disabilities perceive our system, and (c) how the mechanical intelligence of Kiri-Spoon complements state-of-the-art algorithms. Our results suggest that Kiri-Spoon advances robot-assisted feeding across diverse foods, multiple robotic platforms, and different manipulation algorithms.

Stable-BC: Controlling Covariate Shift with Stable Behavior Cloning

Behavior cloning is a common imitation learning paradigm. Under behavior cloning the robot collects expert demonstrations, and then trains a policy to match the actions taken by the expert. This works well when the robot learner visits states where the expert has already demonstrated the correct action; but inevitably the robot will also encounter new states outside of its training dataset. If the robot learner takes the wrong action at these new states it could move farther from the training data, which in turn leads to increasingly incorrect actions and compounding errors. Existing works try to address this fundamental challenge by augmenting or enhancing the training data. By contrast, in our paper we develop the control theoretic properties of behavior cloned policies. Specifically, we consider the error dynamics between the system's current state and the states in the expert dataset. From the error dynamics we derive model-based and model-free conditions for stability: under these conditions the robot shapes its policy so that its current behavior converges towards example behaviors in the expert dataset. In practice, this results in Stable-BC, an easy to implement extension of standard behavior cloning that is provably robust to covariate shift. We demonstrate the effectiveness of our algorithm in simulations with interactive, nonlinear, and visual environments. We also conduct experiments where a robot arm uses Stable-BC to play air hockey. See our website here: https://collab.me.vt.edu/Stable-BC/

PECAN: Personalizing Robot Behaviors through a Learned Canonical Space

Robots should personalize how they perform tasks to match the needs of individual human users. Today's robot achieve this personalization by asking for the human's feedback in the task space. For example, an autonomous car might show the human two different ways to decelerate at stoplights, and ask the human which of these motions they prefer. This current approach to personalization is indirect: based on the behaviors the human selects (e.g., decelerating slowly), the robot tries to infer their underlying preference (e.g., defensive driving). By contrast, our paper develops a learning and interface-based approach that enables humans to directly indicate their desired style. We do this by learning an abstract, low-dimensional, and continuous canonical space from human demonstration data. Each point in the canonical space corresponds to a different style (e.g., defensive or aggressive driving), and users can directly personalize the robot's behavior by simply clicking on a point. Given the human's selection, the robot then decodes this canonical style across each task in the dataset -- e.g., if the human selects a defensive style, the autonomous car personalizes its behavior to drive defensively when decelerating, passing other cars, or merging onto highways. We refer to our resulting approach as PECAN: Personalizing Robot Behaviors through a Learned Canonical Space. Our simulations and user studies suggest that humans prefer using PECAN to directly personalize robot behavior (particularly when those users become familiar with PECAN), and that users find the learned canonical space to be intuitive and consistent. See videos here: https://youtu.be/wRJpyr23PKI

VIEW: Visual Imitation Learning with Waypoints

Robots can use Visual Imitation Learning (VIL) to learn everyday tasks from video demonstrations. However, translating visual observations into actionable robot policies is challenging due to the high-dimensional nature of video data. This challenge is further exacerbated by the morphological differences between humans and robots, especially when the video demonstrations feature humans performing tasks. To address these problems we introduce Visual Imitation lEarning with Waypoints (VIEW), an algorithm that significantly enhances the sample efficiency of human-to-robot VIL. VIEW achieves this efficiency using a multi-pronged approach: extracting a condensed prior trajectory that captures the demonstrator's intent, employing an agent-agnostic reward function for feedback on the robot's actions, and utilizing an exploration algorithm that efficiently samples around waypoints in the extracted trajectory. VIEW also segments the human trajectory into grasp and task phases to further accelerate learning efficiency. Through comprehensive simulations and real-world experiments, VIEW demonstrates improved performance compared to current state-of-the-art VIL methods. VIEW enables robots to learn a diverse range of manipulation tasks involving multiple objects from arbitrarily long video demonstrations. Additionally, it can learn standard manipulation tasks such as pushing or moving objects from a single video demonstration in under 30 minutes, with fewer than 20 real-world rollouts. Code and videos here: https://collab.me.vt.edu/view/