no code implementations • 21 May 2025 • Kefan Song, Amir Moeini, Peng Wang, Lei Gong, Rohan Chandra, Yanjun Qi, Shangtong Zhang
At the next round, we prompt the LLM again with the same task and a context consisting of all previous responses and rewards.
no code implementations • 25 Mar 2025 • Jiuqi Wang, Rohan Chandra, Shangtong Zhang
The main contribution of this paper is to propose a new hypothesis that experience replay addresses the loss of plasticity in continual learning.
no code implementations • 10 Mar 2025 • Kefan Song, Jin Yao, Runnan Jiang, Rohan Chandra, Shangtong Zhang
By using expert-written text from arXiv, we are able to benchmark the group fairness of reward models without requiring the same prompt questions across different demographic groups.
no code implementations • 11 Feb 2025 • Amir Moeini, Jiuqi Wang, Jacob Beck, Ethan Blaser, Shimon Whiteson, Rohan Chandra, Shangtong Zhang
Reinforcement learning (RL) agents typically optimize their policies by performing expensive backward passes to update their network parameters.
no code implementations • 28 Dec 2024 • Xijun Wang, Pedro Sandoval-Segura, ChengYuan Zhang, Junyun Huang, Tianrui Guan, Ruiqi Xian, Fuxiao Liu, Rohan Chandra, Boqing Gong, Dinesh Manocha
Addressing this gap, we present a new dataset, DAVE, designed for evaluating perception methods with high representation of Vulnerable Road Users (VRUs: e. g. pedestrians, animals, motorbikes, and bicycles) in complex and unpredictable environments.
no code implementations • 7 Aug 2024 • Chen Tang, Ben Abbatematteo, Jiaheng Hu, Rohan Chandra, Roberto Martín-Martín, Peter Stone
Reinforcement learning (RL), particularly its combination with deep neural networks referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications, suggesting its potential for enabling the development of sophisticated robotic behaviors.
no code implementations • 26 May 2024 • Rohan Chandra, Haresh Karnan, Negar Mehr, Peter Stone, Joydeep Biswas
In this paper, we present a new multi-agent maximum entropy inverse reinforcement learning algorithm for real world unstructured pedestrian crowds.
no code implementations • 8 May 2024 • Chirag Parikh, Ravi Shankar Mishra, Rohan Chandra, Ravi Kiran Sarvadevabhatla
Recognizing driving behaviors is important for downstream tasks such as reasoning, planning, and navigation.
no code implementations • 12 Dec 2023 • Vrushabh Zinage, Rohan Chandra, Efstathios Bakolas
In this paper, we propose a deep learning based control synthesis framework for fast and online computation of controllers that guarantees the safety of general nonlinear control systems with unknown dynamics in the presence of input constraints.
1 code implementation • 29 Sep 2023 • Vrushabh Zinage, Rohan Chandra, Efstathios Bakolas
Towards this aim, we first construct a governing augmented state space model consisting of the equations of motion of the original system, the integral control law and the nonlinear disturbance observer.
no code implementations • 29 Jun 2023 • Anthony Francis, Claudia Pérez-D'Arpino, Chengshu Li, Fei Xia, Alexandre Alahi, Rachid Alami, Aniket Bera, Abhijat Biswas, Joydeep Biswas, Rohan Chandra, Hao-Tien Lewis Chiang, Michael Everett, Sehoon Ha, Justin Hart, Jonathan P. How, Haresh Karnan, Tsang-Wei Edward Lee, Luis J. Manso, Reuth Mirksy, Sören Pirk, Phani Teja Singamaneni, Peter Stone, Ada V. Taylor, Peter Trautman, Nathan Tsoi, Marynel Vázquez, Xuesu Xiao, Peng Xu, Naoki Yokoyama, Alexander Toshev, Roberto Martín-Martín
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation.
1 code implementation • 15 Jun 2023 • Rohan Chandra, Rahul Menon, Zayne Sprague, Arya Anantula, Joydeep Biswas
This paper presents a fully decentralized approach for realtime non-cooperative multi-robot navigation in social mini-games, such as navigating through a narrow doorway or negotiating right of way at a corridor intersection.
1 code implementation • 9 Jun 2023 • Xiyang Wu, Rohan Chandra, Tianrui Guan, Amrit Singh Bedi, Dinesh Manocha
Our approach for intent-aware planning, iPLAN, allows agents to infer nearby drivers' intents solely from their local observations.
1 code implementation • 9 Mar 2023 • Zayne Sprague, Rohan Chandra, Jarrett Holtz, Joydeep Biswas
We present SocialGym 2, a multi-agent navigation simulator for social robot research.
no code implementations • 15 Oct 2022 • Rohan Chandra, Rahul Maligi, Arya Anantula, Joydeep Biswas
We perform an extensive array of experiments that show that optimal search-based MAPF techniques lead to collisions and increased time-to-goal in SocialMAPF compared to our proposed method using mechanism design.
no code implementations • 16 Sep 2021 • Rohan Chandra, Xijun Wang, Mridul Mahajan, Rahul Kala, Rishitha Palugulla, Chandrababu Naidu, Alok Jain, Dinesh Manocha
We present a new traffic dataset, METEOR, which captures traffic patterns and multi-agent driving behaviors in unstructured scenarios.
1 code implementation • 24 Apr 2021 • Tianrui Guan, Jun Wang, Shiyi Lan, Rohan Chandra, Zuxuan Wu, Larry Davis, Dinesh Manocha
We present a novel architecture for 3D object detection, M3DeTR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales based on multi-scale feature pyramids.
Ranked #1 on
3D Object Detection
on KITTI Cyclist Moderate val
1 code implementation • 7 Mar 2021 • Tianrui Guan, Divya Kothandaraman, Rohan Chandra, Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Dinesh Manocha
We interface GANav with a deep reinforcement learning-based navigation algorithm and highlight its benefits in terms of navigation in real-world unstructured terrains.
Ranked #1 on
Semantic Segmentation
on RUGD
2 code implementations • 27 Nov 2020 • Divya Kothandaraman, Rohan Chandra, Dinesh Manocha
We present a novel approach for unsupervised road segmentation in adverse weather conditions such as rain or fog.
3 code implementations • 7 Nov 2020 • Angelos Mavrogiannis, Rohan Chandra, Dinesh Manocha
We address the problem of ego-vehicle navigation in dense simulated traffic environments populated by road agents with varying driver behaviors.
Robotics
1 code implementation • 22 Sep 2020 • Divya Kothandaraman, Rohan Chandra, Dinesh Manocha
We present an unsupervised adaptation approach for visual scene understanding in unstructured traffic environments.
no code implementations • CVPR 2020 • Trisha Mittal, Pooja Guhan, Uttaran Bhattacharya, Rohan Chandra, Aniket Bera, Dinesh Manocha
We report an AP of 65. 83 across 4 categories on GroupWalk, which is also an improvement over prior methods.
Ranked #2 on
Emotion Recognition in Context
on CAER
Emotion Recognition in Context
Multimodal Emotion Recognition
no code implementations • 14 Mar 2020 • Trisha Mittal, Uttaran Bhattacharya, Rohan Chandra, Aniket Bera, Dinesh Manocha
Additionally, we extract and compare affective cues corresponding to perceived emotion from the two modalities within a video to infer whether the input video is "real" or "fake".
no code implementations • arXiv 2019 • Rohan Chandra, Tianrui Guan, Srujan Panuganti, Trisha Mittal, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
In practice, our approach reduces the average prediction error by more than 54% over prior algorithms and achieves a weighted average accuracy of 91. 2% for behavior prediction.
Ranked #1 on
Trajectory Prediction
on ApolloScape
Robotics
no code implementations • ECCV 2020 • Uttaran Bhattacharya, Christian Roncal, Trisha Mittal, Rohan Chandra, Kyra Kapsaskis, Kurt Gray, Aniket Bera, Dinesh Manocha
For the annotated data, we also train a classifier to map the latent embeddings to emotion labels.
no code implementations • 9 Nov 2019 • Trisha Mittal, Uttaran Bhattacharya, Rohan Chandra, Aniket Bera, Dinesh Manocha
Our approach combines cues from multiple co-occurring modalities (such as face, text, and speech) and also is more robust than other methods to sensor noise in any of the individual modalities.
1 code implementation • 28 Oct 2019 • Uttaran Bhattacharya, Trisha Mittal, Rohan Chandra, Tanmay Randhavane, Aniket Bera, Dinesh Manocha
We use hundreds of annotated real-world gait videos and augment them with thousands of annotated synthetic gaits generated using a novel generative network called STEP-Gen, built on an ST-GCN based Conditional Variational Autoencoder (CVAE).
1 code implementation • 20 Jul 2019 • Rohan Chandra, Uttaran Bhattacharya, Christian Roncal, Aniket Bera, Dinesh Manocha
RobustTP is an approach that first computes trajectories using a combination of a non-linear motion model and a deep learning-based instance segmentation algorithm.
Robotics
1 code implementation • 25 Jun 2019 • Rohan Chandra, Uttaran Bhattacharya, Tanmay Randhavane, Aniket Bera, Dinesh Manocha
We present a realtime tracking algorithm, RoadTrack, to track heterogeneous road-agents in dense traffic videos.
Robotics
2 code implementations • CVPR 2019 • Rohan Chandra, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
We evaluate the performance of our prediction algorithm, TraPHic, on the standard datasets and also introduce a new dense, heterogeneous traffic dataset corresponding to urban Asian videos and agent trajectories.
Ranked #1 on
Trajectory Prediction
on TRAF
Trajectory Prediction
Robotics
1 code implementation • 23 Dec 2017 • Rohan Chandra, Sachin Grover, Kyungjun Lee, Moustafa Meshry, Ahmed Taha
A novel loss function, FLTBNK, is used for training the texture synthesizer.