publications

2026

1. DGT-Map: Directional Global Traversability Mapping Utilizing Multi-Task Learning for Heterogeneous Vehicles

   Jaskrit Singh, Kashif K. Noori, Jing Xiao, and Constantinos Chamzas

   arXiv preprint arXiv:2609.30461, Sep 2026

   Under review

   Abs DOI arXiv Bib HTML PDF

   Off-road traversability is direction-dependent and vehicle specific, yet most global maps assign a single isotropic cost to each location. Existing learned estimators are also commonly trained independently for each vehicle; this preserves vehicle-specific behavior but prevents vehicles from sharing common terrain representations. DGT-MAP addresses both limitations through a self-supervised framework that learns global, directional, and vehicle-conditioned traversability costmaps from RGB-D observations and locomotion signals. A shared multi-task backbone learns common terrain features across training vehicles while vehicle-specific prediction heads preserve platform-dependent responses. At inference, DGT-MAP produces a heading-indexed costmap that can be used by a direction-aware planner. We evaluate DGT-MAP in simulation by integrating it into a Hybrid A* navigation stack and measuring downstream task success on challenging terrains, including slopes that are traversable downhill but not uphill and a ridge obstacle that is traversable by some vehicles, but not by others. Across evaluated tasks, DGT-MAP achieves the highest or tied-highest navigation success rate when compared against geometric, binary, and learned direction-agnostic baselines.

   @article{singh2026dgt,
     title = {{DGT-Map: Directional Global Traversability Mapping Utilizing Multi-Task Learning for Heterogeneous Vehicles}},
     author = {Singh, Jaskrit and Noori, Kashif K. and Xiao, Jing and Chamzas, Constantinos},
     year = {2026},
     month = sep,
     journal = {arXiv preprint arXiv:2609.30461},
     note = {Under review},
     archiveprefix = {arXiv},
     primaryclass = {cs.RO},
     doi = {10.48550/arXiv.2609.30461},
   }

2. Asymmetric Scout-Worker Reconnaissance for Route Validation in Unknown Environments

   Kashif Khurshid Noori, Jaskrit Singh, Athanasios Ch. Kapoutsis, Jing Xiao, and Constantinos Chamzas

   arXiv preprint arXiv:2609.36537, Sep 2026

   Accepted to ISRR

   Abs DOI arXiv Bib HTML PDF

   This paper studies asymmetric scout-worker reconnaissance in unknown environments, where a small, agile autonomous scout explores routes for a larger worker robot that must visit an ordered sequence of goal locations. Because the scout has a smaller footprint and greater mobility, a scout-traversable route may be infeasible for the worker; worker feasibility must therefore be inferred from scout observations. This setting is not explicitly addressed by existing exploration and replanning methods, which typically assume a single traversability model and seek optimal paths for the same robot performing the exploration. We introduce a symbiotic scout-based framework that exploits the scout’s superior mobility to explore only the portions of the unknown environment needed to identify worker-feasible path segments connecting the ordered goals. Evaluations in simulated and real-world settings demonstrate that the proposed approach validates feasible routes, repairs blocked segments with validated worker-feasible detours, and substantially reduces scout travel compared to baseline exploration and planning methods. A real-world indoor deployment further demonstrates the scout navigating narrow corridors to identify a worker-feasible route.

   @article{noori2026scout,
     title = {{Asymmetric Scout-Worker Reconnaissance for Route Validation in Unknown Environments}},
     author = {Noori, Kashif Khurshid and Singh, Jaskrit and Kapoutsis, Athanasios Ch. and Xiao, Jing and Chamzas, Constantinos},
     year = {2026},
     month = sep,
     journal = {arXiv preprint arXiv:2609.36537},
     note = {Accepted to ISRR},
     archiveprefix = {arXiv},
     primaryclass = {cs.RO},
     doi = {10.48550/arXiv.2609.36537},
   }

3. MetaPusher: Meta Learning and Planning for Nonprehensile Manipulation of Unseen Objects with Rapid Online Adaption

   Donghyung Lee, Seyedali Golestaneh, Jaskrit Singh, Zhuoyun Zhong, Athanasios Kapoutsis, and Constantinos Chamzas

   arXiv preprint arXiv:2609.21122, Sep 2026

   Under review

   Abs DOI arXiv Bib HTML PDF

   Manipulating previously unseen objects remains challenging, as their dynamics depend on latent physical properties, such as friction and mass distribution, that cannot be inferred from perception alone. Prior experience across objects can provide an initial estimate of unseen object dynamics, but this estimate remains uncertain and can degrade further during sim-to-real transfer. Adapting the dynamics through interaction can progressively refine the estimation, however, updating the model may invalidate the planned trajectory. Successful and efficient manipulation therefore requires both rapid dynamics adaptation and a planning strategy that can incorporate this evolution. In this work, we introduce MetaPusher, a meta-learning and adaptive planning framework for nonprehensile manipulation of unseen objects without prior object-specific interactions. A meta-learned dynamics model rapidly adapts from interactions during task execution, while an adaptive kinodynamic planner updates long-horizon plans by reusing and refining its existing search tree. This coupling enables manipulation and adaptation without a separate data collection phase. We evaluate MetaPusher on unseen objects in simulation and in sim-to-real scenarios, comparing against fine-tuning and active learning methods, MPPI-based control, and a reinforcement learning policy. It achieves lower prediction error and improves task success rate by up to 20%.

   @article{lee2026metapusher,
     title = {{MetaPusher: Meta Learning and Planning for Nonprehensile Manipulation of Unseen Objects with Rapid Online Adaption}},
     author = {Lee, Donghyung and Golestaneh, Seyedali and Singh, Jaskrit and Zhong, Zhuoyun and Kapoutsis, Athanasios and Chamzas, Constantinos},
     year = {2026},
     month = sep,
     journal = {arXiv preprint arXiv:2609.21122},
     note = {Under review},
     archiveprefix = {arXiv},
     primaryclass = {cs.RO},
     doi = {10.48550/arXiv.2609.21122},
   }