Beyond Exteroception Interoceptive Perception for Resilient Robotics

Modern robots increasingly rely on external sensors—cameras, LiDARs, and radars—as their primary perceptual modality. Yet biological organisms evolved a fundamentally different strategy: they first understand their own body through vestibular and proprioceptive feedback before interpreting the external world. This workshop explores internal perception, the use of inertial measurement units (IMUs), joint encoders, force/torque sensors, and other body-mounted proprioceptive sensors, as a primary, not auxiliary, source of perceptual intelligence for resilient robotics.

We argue that robust autonomy demands perception systems that are not only world-facing but also self-aware of their motion, dynamics, and physical state. This is not a metaphorical notion but a principled research direction centered on inertial sensing, proprioception, and their integration with external perception. Topics span learning-based inertial odometry, cross-embodiment proprioceptive motion model, adaptive sensor fusion under degradation, and the emerging role of humanoid robots as testbeds for internal-sensing research. The workshop brings together researchers from state estimation, legged locomotion, inertial navigation, and neuroscience-inspired robotics to define the foundations of this underexplored paradigm. Featuring invited talks, a contributed poster session, a panel discussion, and the inaugural Learning IMU Odometry Challenge, this workshop aims to catalyze a community around instinct-like perception for resilient robots.

The workshop date is tentative. Live competition rules and submission deadlines on Kaggle remain the source of truth.

Robots need to understand both the world around them and the state of their own bodies. This workshop examines inertial measurement units, joint encoders, force/torque sensing, and other proprioceptive signals as primary sources of perceptual intelligence—not merely auxiliary inputs to vision and LiDAR pipelines.

The program connects state estimation, legged locomotion, inertial navigation, humanoid robotics, and learning-based perception. Invited talks, challenge spotlights, contributed posters, a panel discussion, and open networking will focus on systems that remain reliable when external sensing is degraded or unavailable.

Topics

• Learning-based inertial odometry and navigation
• IMU foundation models and cross-platform generalization
• Proprioceptive state estimation for legged and humanoid robots
• Multi-IMU fusion and spatial-temporal calibration
• Adaptive sensor fusion under environmental degradation
• Online adaptation and self-supervised learning
• Vestibular and proprioceptive inspiration from neuroscience
• Sim-to-real transfer for internal perception
• Robustness benchmarks and evaluation metrics
• Contact-rich and force-aware state estimation
• Differentiable factor graphs and learned optimization
• Integration with visual and geometric foundation models

Who should attend: researchers, students, and practitioners working in state estimation, inertial navigation, robot learning, legged or humanoid robotics, sensor fusion, and resilient autonomy. No specialized workshop prerequisite is required.

Corresponding Organizers

Guanya Shi

Assistant Professor, Robotics Institute

Carnegie Mellon University

Wenshan Wang

Systems Scientist, Robotics Institute

Carnegie Mellon University

Shibo Zhao

Ph.D. Candidate, Robotics Institute

Carnegie Mellon University

Main Organizers

Sebastian Scherer

Research Professor, Robotics Institute

Carnegie Mellon University

Chen Wang

Assistant Professor, Computer Science and Engineering

University at Buffalo

Muqing Cao

Postdoc, Robotics Institute

Carnegie Mellon University

Junyi Geng

Assistant Professor, Aerospace Engineering

Pennsylvania State University

Yuheng Qiu

Ph.D. Student, Mechanical Engineering

Carnegie Mellon University

Sifan Zhou

Ph.D. Student

Carnegie Mellon University