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1. Understanding human co-manipulation via motion and haptic information to enable future physical human-robotic collaborations

   https://doi.org/10.3389/fnbot.2025.1480399  

   Shaw, Kody; Salmon, John L; Killpack, Marc D (June 2025, Frontiers in Neurorobotics)

   Human teams intuitively and effectively collaborate to move large, heavy, or unwieldy objects. However, understanding of this interaction in literature is limited. This is especially problematic given our goal to enable human-robot teams to work together. Therefore, to better understand how human teams work together to eventually enable intuitive human-robot interaction, in this paper we examine four sub-components of collaborative manipulation (co-manipulation), using motion and haptics. We define co-manipulation as a group of two or more agents collaboratively moving an object. We present a study that uses a large object for co-manipulation as we vary the number of participants (two or three) and the roles of the participants (leaders or followers), and the degrees of freedom necessary to complete the defined motion for the object. In analyzing the results, we focus on four key components related to motion and haptics. Specifically, we first define and examine a static or rest state to demonstrate a method of detecting transitions between the static state and an active state, where one or more agents are moving toward an intended goal. Secondly, we analyze a variety of signals (e.g. force, acceleration, etc.) during movements in each of the six rigid-body degrees of freedom of the co-manipulated object. This data allows us to identify the best signals that correlate with the desired motion of the team. Third, we examine the completion percentage of each task. The completion percentage for each task can be used to determine which motion objectives can be communicated via haptic feedback. Finally, we define a metric to determine if participants divide two degree-of-freedom tasks into separate degrees of freedom or if they take the most direct path. These four components contribute to the necessary groundwork for advancing intuitive human-robot interaction. 

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2. X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations

   Pace, Maximus A; Dan, Prithwish; Ning, Chuanruo; Bhardwaj, Atiksh; Du, Audrey; Duan, Edward W; Ma, Wei-Chiu; Kedia, Kushal (June 2025, ICRA 2026)

   Human videos are a scalable source of training data for robot learning. However, humans and robots significantly differ in embodiment, making many human actions infeasible for direct execution on a robot. Still, these demonstrations convey rich object-interaction cues and task intent. Our goal is to learn from this coarse guidance without transferring embodiment-specific, infeasible execution strategies. Recent advances in generative modeling tackle a related problem of learning from low-quality data. In particular, Ambient Diffusion is a recent method for diffusion modeling that incorporates low-quality data only at high-noise timesteps of the forward diffusion process. Our key insight is to view human actions as noisy counterparts of robot actions. As noise increases along the forward diffusion process, embodiment-specific differences fade away while task-relevant guidance is preserved. Based on these observations, we present X-Diffusion, a cross-embodiment learning framework based on Ambient Diffusion that selectively trains diffusion policies on noised human actions. This enables effective use of easy-to-collect human videos without sacrificing robot feasibility. Across five real-world manipulation tasks, we show that X-Diffusion improves average success rates by 16% over naive co-training and manual data filtering. 

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3. Trends in Haptic Communication of Human-Human Dyads: Toward Natural Human-Robot Co-manipulation

   https://doi.org/10.3389/fnbot.2021.626074  

   Jensen, Spencer W.; Salmon, John L.; Killpack, Marc D. (February 2021, Frontiers in Neurorobotics)

   In this paper, we analyze and report on observable trends in human-human dyads performing collaborative manipulation (co-manipulation) tasks with an extended object (object with significant length). We present a detailed analysis relating trends in interaction forces and torques with other metrics and propose that these trends could provide a way of improving communication and efficiency for human-robot dyads. We find that the motion of the co-manipulated object has a measurable oscillatory component. We confirm that haptic feedback alone represents a sufficient communication channel for co-manipulation tasks, however we find that the loss of visual and auditory channels has a significant effect on interaction torque and velocity. The main objective of this paper is to lay the essential groundwork in defining principles of co-manipulation between human dyads. We propose that these principles could enable effective and intuitive human-robot collaborative manipulation in future co-manipulation research. 

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4. Classification of Co-Manipulation Modus with Human-Human Teams for Future Application to Human-Robot Systems

   https://doi.org/10.1145/3659059  

   Freeman, Seth; Moss, Shaden; Salmon, John L; Killpack, Marc D (October 2024, ACM Transactions on Human-Robot Interaction)

   Despite the existence of robots that can physically lift heavy loads, robots that can collaborate with people to move heavy objects are not readily available. This article makes progress toward effective human-robot co-manipulation by studying 30 human-human dyads that collaboratively manipulated an object weighing\(27 \mathrm{kg}\)without being co-located (i.e., participants were at either end of the extended object). Participants maneuvered around different obstacles with the object while exhibiting one of four modi–the manner or objective with which a team moves an object together–at any given time. Using force and motion signals to classify modus or behavior was the primary objective of this work. Our results showed that two of the originally proposed modi were very similar, such that one could effectively be removed while still spanning the space of common behaviors during our co-manipulation tasks. The three modi used in classification werequickly,smoothlyandavoiding obstacles. Using a deep convolutional neural network (CNN), we classified three modi with up to 89% accuracy from a validation set. The capability to detect or classify modus during co-manipulation has the potential to greatly improve human-robot performance by helping to define appropriate robot behavior or controller parameters depending on the objective or modus of the team. 

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5. Leveraging Submovements for Prediction and Trajectory Planning for Human-Robot Handover

   https://doi.org/10.1145/3529190.3529220  

   Lockwood, Kyle; Bicer, Yunus; Asghari-Esfeden, Sadjad; Zhu, Tianjie; Furmanek, Mariusz; Mangalam, Madhur; Strenge, Garrit; Imbiriba, Tales; Yarossi, Mathew; Padir, Taskin; et al (June 2022, Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments)

   The effectiveness of human-robot interactions critically depends on the success of computational efforts to emulate human inference of intent, anticipation of action, and coordination of movement. To this end, we developed two models that leverage a well described feature of human movement: Gaussian-shaped submovements in velocity profiles, to act as robotic surrogates for human inference and trajectory planning in a handover task. We evaluated both models based on how early in a handover movement the inference model can obtain accurate estimates of handover location and timing, and how similar model trajectories are to human receiver trajectories. Initial results using one participant dyad demonstrate that our inference model can accurately predict location and handover timing, while the trajectory planner can use these predictions to provide a human-like trajectory plan for the robot. This approach delivers promising performance while remaining grounded in physiologically meaningful Gaussian-shaped velocity profiles of human motion. 

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