Papers accepted at IROS 2022 Conference

Three SLMC papers accepted at IROS 2022

Three SLMC papers (one with IEEE-RAL option) have been accepted at the International Conference on Intelligent Robots and Systems (IROS2022) to be held in Kyoto, Japan. 


  • Ran Long, Christian Rauch, Tianwei Zhang, Vladimir Ivan, Tin Lun Lam and Sethu Vijayakumar, RGB-D SLAM in Indoor Planar Environments with Multiple Large Dynamic Objects, IEEE Robotics and Automation Letters (RAL), vol. 7(3), pp. 8209-8216 (2022)  [pdf] [DOI] [video] [citation]

This work presents a novel dense RGB-D SLAM approach for dynamic planar environments that enables simultaneous multi-object tracking, camera localisation and background reconstruction. Previous dynamic SLAM methods either rely on semantic segmentation to directly detect dynamic objects; or assume that dynamic objects occupy a smaller proportion of the camera view than the static background and can, therefore, be removed as outliers. With the aid of camera motion prior, our approach enables dense SLAM when the camera view is largely occluded by multiple dynamic objects. The dynamic planar objects are separated by their different rigid motions and tracked independently. The remaining dynamic non-planar areas are removed as outliers and not mapped into the background. The evaluation demonstrates that our approach outperforms the state-of-the-art methods in terms of localisation, mapping, dynamic segmentation and object tracking. We also demonstrate its robustness to large drift in the camera motion prior.


  • Traiko Dinev, Carlos Mastalli, Vladimir Ivan, Steve Tonneau and Sethu Vijayakumar,  A Versatile Co-Design Approach For Dynamic Legged Robots, Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan (2022). [pdf] [video] [citation]

We present a versatile framework for the computational co-design of legged robots and dynamic maneuvers. Current state-of-the-art approaches are typically based on random sampling or concurrent optimization. We propose a novel bilevel optimization approach that exploits the derivatives of the motion planning sub-problem (i.e., the lower level). These motion-planning derivatives allow us to incorporate arbitrary design constraints and costs in an general-purpose nonlinear program (i.e., the upper level). Our approach allows for the use of any differentiable motion planner in the lower level and also allows for an upper level that captures arbitrary design constraints and costs. It efficiently optimizes the robot’s morphology, payload distribution and actuator parameters while considering its full dynamics, joint limits and physical constraints such as friction cones. We demonstrate these capabilities by designing quadruped robots that jump and trot. We show that our method is able to design a more energy-efficient Solo robot for these tasks.


  • Jiayi Wang, Teguh Santoso Lembono, Sanghyun Kim, Sylvain Calinon, Sethu Vijayakumar and Steve Tonneau, Learning to Guide Online Multi-Contact Receding Horizon Planning, Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan (2022). [pdf] [video] [citation]

In Receding Horizon Planning (RHP), it is critical that the motion being executed facilitates the completion of the task, e.g. building momentum to overcome large obstacles. This requires a value function to inform the desirability of robot states. However, given the complex dynamics, value functions are often approximated by expensive computation of trajectories in an extended planning horizon. In this work, to achieve online multi-contact Receding Horizon Planning (RHP), we propose to learn an oracle that can predict local objectives (intermediate goals) for a given task based on the current robot state and the environment. Then, we use these local objectives to construct local value functions to guide a short-horizon RHP. To obtain the oracle, we take a supervised learning approach, and we present an incremental training scheme that can improve the prediction accuracy by adding demonstrations on how to recover from failures. We compare our approach against the baseline (long-horizon RHP) for planning centroidal trajectories of humanoid walking on moderate slopes as well as large slopes where static stability cannot be achieved. We validate these trajectories by tracking them via a whole-body inverse dynamics controller in simulation. We show that our approach can achieve online RHP for 95%-98.6% cycles, outperforming the baseline (8%-51.2%).


Further Information