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1. Path Planning for Continuum Arms in Dynamic Environments

   https://doi.org/10.1109/RoboSoft60065.2024.10521950  

   Meng, Brandon H; Arachchige, Dimuthu_D K; Godage, Isuru S; Kanj, Iyad (April 2024, IEEE)

   Multisection continuum arms are bio-inspired manipulators that combine compliance, payload, dexterity, and safety to serve as co-robots in human-robot collaborative domains. Their hyper redundancy and complex kinematics, however, pose many challenges when performing path planning, especially in dynamic environments. In this paper, we present a W-Space based Rapidly Exploring Random Trees * path planner for multisection continuum arm robots in dynamic environments. The proposed planner improves the existing state-of-art planners in terms of computation time and the success rate, while removing the need for offline computation. On average, the computation time of our approach is below 2 seconds, and its average success rate is around 70 %. The computation time of the proposed planner significantly improves that of the state-of-the-art planner by roughly a factor of 20, making the former suitable for real-time applications. Moreover, for application domains where the obstacle motion is not very predictable (e.g., human obstacles), the proposed planner significantly improves the success rate of state-of-the-art planners by nearly 50 %. Lastly, we demonstrate the feasibility of several generated trajectories by replicating the motion on a physical prototype arm. 

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2. Robots that Learn to Safely Influence via Prediction-Informed Reach-Avoid Dynamic Games

   Pandya, Ravi; Liu, Changliu; Bajcsy, Andrea (June 2025, IEEE International Conference on Robotics and Automation)

   Robots can influence people to accomplish their tasks more efficiently: autonomous cars can inch forward at an intersection to pass through, and tabletop manipulators can go for an object on the table first. However, a robot's ability to influence can also compromise the physical safety of nearby people if naively executed. In this work, we pose and solve a novel robust reach-avoid dynamic game which enables robots to be maximally influential, but only when a safety backup control exists. On the human side, we model the human's behavior as goal-driven but conditioned on the robot's plan, enabling us to capture influence. On the robot side, we solve the dynamic game in the joint physical and belief space, enabling the robot to reason about how its uncertainty in human behavior will evolve over time. We instantiate our method, called SLIDE (Safely Leveraging Influence in Dynamic Environments), in a high-dimensional (39-D) simulated human-robot collaborative manipulation task solved via offline game-theoretic reinforcement learning. We compare our approach to a robust baseline that treats the human as a worst-case adversary, a safety controller that does not explicitly reason about influence, and an energy-function-based safety shield. We find that SLIDE consistently enables the robot to leverage the influence it has on the human when it is safe to do so, ultimately allowing the robot to be less conservative while still ensuring a high safety rate during task execution. Project website: https://cmu-intentlab.github.io/safe-influence/ 

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3. Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation

   https://doi.org/10.15607/RSS.2021.XVII.053  

   Sun, Muchen; Baldini, Francesca; Trautman, Peter; Murphey, Todd (July 2021, Robotics: Science and Systems)

   null (Ed.)

   Cooperatively avoiding collision is a critical functionality for robots navigating in dense human crowds, failure of which could lead to either overaggressive or overcautious behavior. A necessary condition for cooperative collision avoidance is to couple the prediction of the agents’ trajectories with the planning of the robot’s trajectory. However, it is unclear that trajectory based cooperative collision avoidance captures the correct agent attributes. In this work we migrate from trajectory based coupling to a formalism that couples agent preference distributions. In particular, we show that preference distributions (probability density functions representing agents’ intentions) can capture higher order statistics of agent behaviors, such as willingness to cooperate. Thus, coupling in distribution space exploits more information about inter-agent cooperation than coupling in trajectory space. We thus introduce a general objective for coupled prediction and planning in distribution space, and propose an iterative best response optimization method based on variational analysis with guaranteed sufficient decrease. Based on this analysis, we develop a sampling-based motion planning framework called DistNav1 that runs in real time on a laptop CPU. We evaluate our approach on challenging scenarios from both real world datasets and simulation environments, and benchmark against a wide variety of model based and machine learning based approaches. The safety and efficiency statistics of our approach outperform all other models. Finally, we find that DistNav is competitive with human safety and efficiency performance. 

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4. A Humanoid Robot Object Perception Approach Using Depth Images

   https://doi.org/10.1109/NAECON46414.2019.9057808  

   Cofield, Aaron; El-Shair, Zaid; Rawashdeh, Samir A. (January 2019, Proceedings of IEEE National Aerospace and Electronics Conference (NAECON))

   null (Ed.)

   Humanoid robots have had significant research interest in the past two decades. Their classification as mobile manipulators allows them to work in unstructured environments creating new possibilities for human-robot interaction. Object grasping and manipulation are essential and enabling capabilities for mobile humanoid robots that require reliable perception. This paper presents a perception approach using depth images from an RGB-D camera to estimate the work plane and estimate object positions relative to the robot. Results from experiments with a set of object shapes and scenarios are presented. 

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5. Self-Reflective Learning Strategy for Persistent Autonomy of Aerial Manipulators

   https://doi.org/10.1115/DSCC2019-9086  

   Zhou, Xu; Zhang, Jiucai; Zhang, Xiaoli (October 2019, ASME 2019 Dynamic Systems and Control Conference)

   Abstract Autonomous aerial manipulators have great potentials to assist humans or even fully automate manual labor-intensive tasks such as aerial cleaning, aerial transportation, infrastructure repair, and agricultural inspection and sampling. Reinforcement learning holds the promise of enabling persistent autonomy of aerial manipulators because it can adapt to different situations by automatically learning optimal policies from the interactions between the aerial manipulator and environments. However, the learning process itself could experience failures that can practically endanger the safety of aerial manipulators and hence hinder persistent autonomy. In order to solve this problem, we propose for the aerial manipulator a self-reflective learning strategy that can smartly and safely finding optimal policies for different new situations. This self-reflective manner consists of three steps: identifying the appearance of new situations, re-seeking the optimal policy with reinforcement learning, and evaluating the termination of self-reflection. Numerical simulations demonstrate, compared with conventional learning-based autonomy, our strategy can significantly reduce failures while still can finish the given task. 

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