Srikrishna Bangalore Raghu

Publications

  1. Figure 1 of Dance2Hesitate

    Dance2Hesitate: A Multi-Modal Dataset of Dancer-Taught Hesitancy for Understandable Robot Motion

    S. Bangalore Raghu, A. Soukhovei, D. Vankineni, A. Bacula, and A. Roncone

    Designing Transparent and Understandable Robots (D-TUR) Workshop at HRI · 2026

    Releases a 220-trajectory multimodal dataset of hesitant motion, taught to a Franka Panda by dancers through kinesthetic teaching and recorded as synchronized 30 Hz robot-state streams.

    PDF arXiv Project Data
  2. Figure 1 of the Laban Shape paper

    Employing Laban Shape for Generating Emotionally and Functionally Expressive Trajectories in Robotic Manipulators

    S. Bangalore Raghu, C. Lohrmann, A. Bakshi, J. Kim, J. C. Herrera, B. Hayes, and A. Roncone

    IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) · 2025

    Uses Laban Movement Analysis to generate emotionally and functionally expressive manipulator trajectories. Human trials supported two of three hypotheses.

    PDF arXiv Project
  3. Figure 1 of BBoE

    BBoE: Leveraging a Bundle of Edges for Kinodynamic Bidirectional Motion Planning

    S. Bangalore Raghu and A. Roncone

    IEEE Robotics and Automation Letters (RA-L) · 2026 · Accepted

    Introduces BBoE, a bidirectional kinodynamic planner that leverages a bundle of edges, the only planner in the benchmark to reach 96% success on all four systems (3D to 6D) with the lowest planning time on three.

  4. Figure 1 of the KiTE-Extend paper

    Efficient Multi-Robot Motion Planning with Precomputed Translation-Invariant Edge Bundles

    H. Gupta, P. Motter, A. Chakrabarty, R. Sodani, S. Bangalore Raghu, A. Roncone, B. Hayes, and Z. Sunberg

    arXiv · 2026 · Preprint

    Introduces KiTE-Extend, a planner-agnostic action selection mechanism that guides sampling-based kinodynamic planners with a library of trajectory segments computed offline, with its clearest gains in multi-robot planning.

    PDF arXiv
  5. Figure 1 of the team-based delivery paper

    A Dynamic Heterogeneous Team-based Non-iterative Approach for Online Pick-up and Just-In-Time Delivery Problems

    S. B. Velhal, S. Bangalore Raghu, M. Bharatheesha, and S. Sundaram

    arXiv · 2024 · Preprint

    A non-iterative, team-based method for assigning dynamic heterogeneous robot teams to online pick-up and just-in-time delivery tasks.

    arXiv
Full list on Google Scholar