Kaivalya Agrawal

2026

1. RA-L

   Variational Shape Inference for Grasp Diffusion on SE(3)

   S. Talha Bukhari, Kaivalya Agrawal, Zachary Kingston, and Aniket Bera

   IEEE Robotics and Automation Letters

   Abs Bib PDF Video Code

   Grasp synthesis is a fundamental task in robotic manipulation which usually has multiple feasible solutions. Multimodal grasp synthesis seeks to generate diverse sets of stable grasps conditioned on object geometry, making the robust learning of geometric features crucial for success. To address this challenge, we propose a framework for learning multimodal grasp distributions that leverages variational shape inference to enhance robustness against shape noise and measurement sparsity. Our approach first trains a variational autoencoder for shape inference using implicit neural representations, and then uses these learned geometric features to guide a diffusion model for grasp synthesis on the SE(3) manifold. Additionally, we introduce a test-time grasp optimization technique that can be integrated as a plugin to further enhance grasping performance. Experimental results demonstrate that our shape inference for grasp synthesis formulation outperforms state-of-the-art multimodal grasp synthesis methods on the ACRONYM dataset by 6.3%, while demonstrating robustness to deterioration in point cloud density compared to other approaches. Furthermore, our trained model achieves zero-shot transfer to real-world manipulation of household objects, generating 34% more successful grasps than baselines despite measurement noise and point cloud calibration errors.

   @article{bukhari2025graspdiff,
     author = {Bukhari, S. Talha and Agrawal, Kaivalya and Kingston, Zachary and Bera, Aniket},
     doi = {10.1109/LRA.2025.3645521},
     journal = {IEEE Robotics and Automation Letters},
     number = {2},
     pages = {2170--2177},
     title = {Variational Shape Inference for Grasp Diffusion on SE(3)},
     volume = {11},
     year = {2026}
   }

2. arXiv

   

   SkipVLA: Skipping VLA Steps with Classical Planning for Fast Robot Manipulation

   Kaivalya Agrawal, Md Ashiqur Rahman, Raymond A. Yeh, and Zachary Kingston

   Under Review

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   Vision-Language-Action (VLA) models are a class of generalist robot policies that map camera images and language instructions directly to robot actions. While promising, these models remain slow at test time, particularly for long-horizon tasks that require many queries to the policy. Recent efforts reduce VLA latency by distilling smaller models, overlapping asynchronous action chunks, or pairing the VLA with a fast low-level policy, but still run a learned policy for the entire task. In contrast to VLA, classical motion planners quickly find collision-free motions, but require an explicit goal and have no semantic understanding of the task. In this work, we present SkipVLA, a hybrid policy that combines a pretrained VLA with a classical motion planner, using the planner for free-space motion and querying the VLA only for contact-rich skills such as grasping and placing. SkipVLA reuses the frozen vision-language backbone of the VLA to predict a target pose for each planned motion, and learns this predictor without additional demonstrations introduced into the system by using what was already learnt by the large VLA. We evaluate SkipVLA with three VLAs on 13 LIBERO tasks in simulation and three pick-and-place tasks on a physical 6-DoF YAM arm, demonstrating up to 2.5x faster task completion and significantly lower energy consumption while achieving the same task success rate.

   @misc{agrawal2026skipvla,
     archiveprefix = {arXiv},
     author = {Agrawal, Kaivalya and Rahman, Md Ashiqur and Yeh, Raymond A. and Kingston, Zachary},
     eprint = {2609.20648},
     note = {Under Review},
     primaryclass = {cs.RO},
     title = {SkipVLA: Skipping VLA Steps with Classical Planning for Fast Robot Manipulation},
     year = {2026}
   }