webpage | pdf | bibtex

webpage | bibtex | paper

            @misc{gamma,
              title={GAMMA: Graspability-Aware Mobile MAnipulation Policy Learning based on Online Grasping Pose Fusion}, 
              author={Jiazhao Zhang and Nandiraju Gireesh and Jilong Wang and Xiaomeng Fang and Chaoyi Xu and Weiguang Chen and Liu Dai and He Wang},
              year={2023},
              eprint={2309.15459},
              archivePrefix={arXiv},
              primaryClass={cs.RO}
        }
          

webpage | bibtex | paper

          @misc{sam,
            title={Sequence-Agnostic Multi-Object Navigation}, 
            author={Nandiraju Gireesh and Ayush Agrawal and Ahana Datta and Snehasis Banerjee and Mohan Sridharan and Brojeshwar Bhowmick and Madhava Krishna},
            year={2023},
            eprint={2305.06178},
            archivePrefix={arXiv},
            primaryClass={cs.RO}
      }
        

webpage | bibtex | paper

Agents that are aware of the separation between the environments and themselves can leverage this understanding to form effective representations of visual input. We propose an approach for learning such structured representations for RL algorithms, using visual knowledge of the agent, which is often inexpensive to obtain, such as its shape or mask. This is incorporated into the RL objective using a simple auxiliary loss. We show that our method, SEAR (Structured Environment-Agent Representations), outperforms state-of-the-art model-free approaches over 18 different challenging visual simulation environments spanning 5 different robots.

            @misc{drq,
              title={Object Goal Navigation using Data Regularized Q-Learning}, 
              author={Nandiraju Gireesh and D. A. Sasi Kiran and Snehasis Banerjee and Mohan Sridharan and Brojeshwar Bhowmick and Madhava Krishna},
              year={2022},
              eprint={2208.13009},
              archivePrefix={arXiv},
              primaryClass={cs.RO}
        }
              

webpage | bibtex | paper

Agents that are aware of the separation between the environments and themselves can leverage this understanding to form effective representations of visual input. We propose an approach for learning such structured representations for RL algorithms, using visual knowledge of the agent, which is often inexpensive to obtain, such as its shape or mask. This is incorporated into the RL objective using a simple auxiliary loss. We show that our method, SEAR (Structured Environment-Agent Representations), outperforms state-of-the-art model-free approaches over 18 different challenging visual simulation environments spanning 5 different robots.

            @misc{srg,
              title={Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation}, 
              author={D. A. Sasi Kiran and Kritika Anand and Chaitanya Kharyal and Gulshan Kumar and Nandiraju Gireesh and Snehasis Banerjee and Ruddra dev Roychoudhury and Mohan Sridharan and Brojeshwar Bhowmick and Madhava Krishna},
              year={2022},
              eprint={2208.13031},
              archivePrefix={arXiv},
              primaryClass={cs.RO}
        }
              

webpage | bibtex | paper

          @INPROCEEDINGS{sacabr,

            author={Naresh, Mandan and Gireesh, Nandiraju and Saxena, Paresh and Gupta, Manik},
          
            booktitle={2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS)}, 
          
            title={SAC-ABR: Soft Actor-Critic based deep reinforcement learning for Adaptive BitRate streaming}, 
          
            year={2022},
          
            volume={},
          
            number={},
          
            pages={353-361},
          
            doi={10.1109/COMSNETS53615.2022.9668424}}
          
        

webpage | bibtex | paper

          @misc{xray,
            title={XRayGAN: Consistency-preserving Generation of X-ray Images from Radiology Reports}, 
            author={Xingyi Yang and Nandiraju Gireesh and Eric Xing and Pengtao Xie},
            year={2020},
            eprint={2006.10552},
            archivePrefix={arXiv},
            primaryClass={eess.IV}
      }