@article{sajnani2026uniwalm,
  title = {UniWALM: A Unified World-Action-Language Model for Embodied Robot Control},
  author = {Sajnani, Rahul and Romeres, Diego and Zhang, Mingyuan and Gao, Kai and Qi, Carl and Huang, Baichuan and Sridhar, Srinath and Randle, Dylan and Ratliff, Lillian J. and Barreiros, Jose and Nambi, Manikantan and Vanbaar, Jeroen},
  year = {2027},
  journal = {IEEE International Conference on Robotics and Automation (ICRA) (Under Review)},
  webpage = {/projects/uniwalm/},
  note = {<ul class="tldr"><li>Mixture-of-Transformers (Wan2.2 video + action DiT + <b>discrete-diffusion language expert</b>) co-generates video, action, and language in <b>one parallel diffusion pass</b>.</li><li>Policy re-reads its own generated language as <b>memory</b> on every control step — no external VLM / planner.</li><li>Real-world memory-dependent manipulation: <b>11.1% → 61.1%</b> vs same-backbone FastWAM.</li><li><b>34.5%</b> on RMBench, above Mem-0's privileged-keyframe setup (28.5%).</li></ul>}
}

@article{sajnani2026appearancepointers,
  title = {AppearancePointers: Fine-Grained Multi-Region Control with Appearance Pointers},
  author = {Sajnani, Rahul and Gadelha, Matheus and Gryaditskaya, Yulia and Sridhar, Srinath and Mech, Radom\'{i}r},
  year = {2027},
  journal = {International Conference on Learning Representations (ICLR), 2027 (Under Review)},
  webpage = {/projects/appearance_pointers/},
  note = {<ul class="tldr"><li>FLUX-based model for <b>multi-region controllable generation</b>: each output region independently steered by text, reference image, and material texture in a single denoising pass.</li><li><b>State-of-the-art</b> on three multi-region fidelity metrics, beating DreamRenderer and MSDiffusion at larger scale.</li><li>Introduces the curated <b>AP-37K</b> multimodal dataset.</li></ul>}
}

@article{qi2026flowreversed,
  title = {Sample-Efficient Reinforcement Learning for Policy Improvement via Flow-Reversed Action Anchors},
  author = {Qi, Carl and Dong, Weijun and Wang, Xiaojie and Chen, Peiyi and Sajnani, Rahul and Zhang, Mingyuan and Zhang, Amy and Barreiros, Jose and Nambi, Manikantan and Ratliff, Lillian J. and Randle, Dylan},
  year = {2027},
  journal = {International Conference on Learning Representations (ICLR), 2027 (Under Review)},
  url = {https://arxiv.org/abs/2610.22852},
  webpage = {https://anchorq-rl.com/},
  note = {<ul class="tldr"><li>Sample-efficient <b>RL fine-tuning</b> of generalist robot policies using <b>flow-reversed action anchors</b>.</li><li>A diffusion-policy improvement scheme that sharply reduces costly online interaction.</li></ul>}
}

@inproceedings{sajnani2024geodiffusergeometrybasedimageediting,
  title = {GeoDiffuser: Geometry-Based Image Editing with Diffusion Models},
  author = {Sajnani, Rahul and Vanbaar, Jeroen and Min, Jie and Katyal, Kapil and Sridhar, Srinath},
  year = {2025},
  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  eprint = {2404.14403},
  archiveprefix = {arXiv},
  primaryclass = {cs.CV},
  url = {https://arxiv.org/abs/2404.14403},
  webpage = {/projects/geodiffuser/},
  note = {<ul class="tldr"><li><b>Training-free</b> geometry-based image editing on a frozen T2I diffusion model.</li><li>User specifies a <b>3D rigid transform</b> (rotate, translate, scale); GeoDiffuser rewrites attention maps to realize it.</li><li>No fine-tuning, no 3D supervision — WACV 2025 <b>Best Student Paper</b>.</li></ul>}
}

@inproceedings{genhsi2026,
  title = {GenHSI: Training-Free Controllable Generation of Human-Scene Interaction Videos},
  author = {Li, Zekun and Zhou, Rui and Sajnani, Rahul and Cong, Xiaoyan and Ritchie, Daniel and Sridhar, Srinath},
  year = {2026},
  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  webpage = {https://kunkun0w0.github.io/project/GenHSI/},
  note = {<ul class="tldr"><li><b>Training-free</b> controllable generation of human–scene interaction videos.</li><li>Uses a video-diffusion prior with per-region controls — no retraining for new scenes or humans.</li></ul>}
}

@inproceedings{Agaram_2023_CVPR,
  author = {Agaram, Rohith and Dewan, Shaurya and Sajnani, Rahul and Poulenard, Adrien and Krishna, Madhava and Sridhar, Srinath},
  title = {Canonical Fields: Self-Supervised Learning of Pose-Canonicalized Neural Fields},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = jun,
  year = {2023},
  pages = {4500-4510},
  webpage = {https://ivl.cs.brown.edu/research/canonical-fields.html},
  note = {<ul class="tldr"><li>Self-supervised learning of <b>pose-canonicalized neural fields</b> (NeRFs).</li><li>Discovers a canonical frame for an object category without any pose or 3D supervision.</li><li>CVPR 2023 <b>Highlight</b>.</li></ul>}
}

@inproceedings{Wei_2023_CVPR,
  author = {Wei, Qiuhong Anna and Ding, Sijie and Park, Jeong Joon and Sajnani, Rahul and Poulenard, Adrien and Sridhar, Srinath and Guibas, Leonidas},
  title = {LEGO-Net: Learning Regular Rearrangements of Objects in Rooms},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = jun,
  year = {2023},
  pages = {19037-19047},
  webpage = {https://ivl.cs.brown.edu/research/lego-net.html},
  note = {<ul class="tldr"><li>Learns to <b>rearrange cluttered rooms into regular layouts</b> using a diffusion-based scene editor.</li><li>Discovers regularity patterns (alignment, symmetry) directly from indoor-scene data.</li></ul>}
}

@inproceedings{Sajnani_2022_CVPR,
  author = {Sajnani, Rahul and Poulenard, Adrien and Jain, Jivitesh and Dua, Radhika and Guibas, Leonidas J. and Sridhar, Srinath},
  title = {ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = jun,
  year = {2022},
  pages = {16969-16979},
  webpage = {https://ivl.cs.brown.edu/ConDor/},
  note = {<ul class="tldr"><li><b>SO(3)-equivariant</b> self-supervised 3D pose canonicalization — works on <b>partial</b> point clouds.</li><li>Also performs automatic co-part segmentation as a byproduct.</li></ul>}
}

@article{Sajnani2020DRACOWS,
  title = {DRACO: Weakly Supervised Dense Reconstruction And Canonicalization of Objects},
  author = {Sajnani, Rahul and Sanchawala, AadilMehdi J. and Jatavallabhula, Krishna Murthy and Sridhar, Srinath and Krishna, K. Madhava},
  journal = {2021 IEEE International Conference on Robotics and Automation (ICRA)},
  year = {2021},
  pages = {10302-10309},
  url = {https://api.semanticscholar.org/CorpusID:227161908},
  note = {<ul class="tldr"><li><b>Weakly-supervised</b> dense reconstruction and <b>pose canonicalization</b> of 3D objects.</li><li>Learned without any 3D ground-truth supervision — IEEE ICRA 2021.</li></ul>}
}

@article{Nair2020MultiobjectMS,
  title = {Multi-object Monocular SLAM for Dynamic Environments},
  author = {Nair, Gokul B. and Daga, Swapnil and Sajnani, Rahul and Ramesh, Anirudh and Ansari, Junaid Ahmed and Krishna, K. Madhava},
  journal = {2020 IEEE Intelligent Vehicles Symposium (IV)},
  year = {2020},
  pages = {651-657},
  url = {https://api.semanticscholar.org/CorpusID:211069692},
  note = {<ul class="tldr"><li>Monocular SLAM that <b>explicitly models independent object motion</b> in dynamic driving scenes.</li><li>Handles moving vehicles and pedestrians without filtering them out.</li></ul>}
}