Updates
[Jun '23]   |
Presented our work on the characterizing of human peripheral vision during driving for
intelligent driving assistance at IV 2023 in beautiful Anchorage! |
[May '23]   |
Starting a research internship at Toyota Research Institute, working on object-based representations of driver awareness. Excited to be in Cambridge! |
[Mar '23]   |
Proposed my PhD thesis -- offically a PhD candidate! You can email me to watch a recording of my talk "Eye Gaze for Intelligent Driving". |
[Dec '22]   |
Our work on using using driver eye gaze as a supervisor for imitation learned driving won best paper at the
Aligning Robot Representations with Humans workshop at CoRL 2022! |
[Dec '22]   |
Organized the Attention Learning Workshop at NeurIPS '22 . |
[Jun '22]   |
Starting a research internship at Bosch, exploring the use of human driver eye gaze for supervising imitation learned driving agents. |
[May '22]   |
Grateful to have won a Modeling, Simulation, and Training Fellowship to support my PhD research -- thank you to the Link Foundation! |
[Mar '22]   |
Presented our VR driving simulator DReyeVR at HRI 2023 -- available on GitHub! |
Ongoing work
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Representations of driver situational awareness (SA) based on eye gaze
Building real-time driver situational awareness via a novel interactive driver SA data collection method and object-based representations.
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Research
(*) denotes equal contribution
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Characterizing Drivers' Peripheral Vision via the Functional Field of View for Intelligent Driving Assistance
A Biswas, and
H Admoni
IEEE Intelligent Vehicle Symposium (IV) 2023
Oral: 5% acceptance rate
Also appeared as a peer-reviewed talk at CogSci 23
[Pre-print]
We find that driver peripheral vision is vertically asymmetrical -- more peripheral stimuli are missed
in the upper portion of drivers FoV (only while driving).
Also, right after saccades (eye movements), driver peripheral vision degrades.
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Mitigating Causal Confusion in Driving Agents via Gaze Supervision
A Biswas,
BA Pardhi,
C Chuck,
J Holtz,
S Niekum,
H Admoni, and
A Allievi
Aligning Robot Representations with Humans (ARRH) workshop at Conference on Robot Learning 2022
[NVIDIA best paper award @ CoRL ARRH workshop]
[Pre-print]
While driving, human drivers naturally exhibit an easily obtained, continuous signal that is highly correlated with causal elements of the state
space: eye gaze. How can we use it as a supervisory signal?
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DReyeVR: Democratizing Virtual Reality Driving Simulation for Behavioural & Interaction Research
G Silvera*,
A Biswas*, and
H Admoni
ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2022,
Short Contributions Track
[arXiv]
[Simulator Github]
[Video]
We open-source DReyeVR, our VR-based driving simulator built with human-centric research in mind.
It's based on CARLA -- if CARLA is for algorithmic drivers, DReyeVR is for humans.
The hardware setup is affordable for many academic labs, costing under 5000 USD.
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SocNavBench: A Grounded Simulation Testing Framework for Evaluating Social Navigation
A Biswas,
A Wang,
G Silvera,
A Steinfeld, and
H Admoni
ACM Transactions on Human-Robot Interaction (THRI) 2021,
Special Issue: Test Methods for Human-Robot Teaming Performance Evaluations
[Paper]
[Pre-print]
[Simulator]
[Baselines]
We introduce SocNavBench, a simulation framework for evaluating social navigation algorithms in a consistent and interpretable manner.
It has a simulator with photo-realistic capabilities, curated social navigation scenarios grounded in real-world pedestrian data, and a suite of metrics that is auto-computed.
Try it out to evaluate your own social navigation algorithms!
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Examining the Effects of Anticipatory Robot Assistance on Human Decision Making
B Newman*,
A Biswas*,
S Ahuja,
S Girdhar, and
H Admoni
International Conference on Social Robotics (ICSR) 2020
[Paper]
[Video]
Does preemptive robot assistance change human decision making?
We show in an experiment (N=99), that people's decision making in a selection task
does change in response to anticipatory robot assistance, but predicting the direction of change is difficult.
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Human Torso Pose Forecasting in the Real World
A Biswas,
H Admoni, and
A Steinfeld
Multi-modal Perception and Control Workshop, Robotics:Science and Systems (RSS) 2018
[Paper]
[More results]
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SketchParse: Towards Rich Descriptions for Poorly Drawn Sketches using Multi-Task Hierarchical Deep Networks
RK Sarvadevabhatla,
I Dwivedi,
A Biswas,
S Manocha, and
R V Babu
ACM Multimedia Conference (ACM MM) 2017
[arXiv]
[Code]
Can we use neural networks to semantically parse freehand sketches?
We show this is possible by "sketchifying" natural images to generate training data and employing a graphical model for generating descriptions.
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Development of an Assistive Stereo Vision System
T Shankar,
A Biswas, and
V Arun
International Convention on Rehabilitation Engineering & Assistive Technology, (i-CREATe) 2015
[Paper]
[News]
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First-order Meta-Learned Initialization for Faster Adaptation in Deep Reinforcement Learning
Abhijat Biswas, Shubham Agrawal
[Report]
First-derivative approximations to meta-learning updates perform just as well as second-derivative ones. Demonstrated on RL tasks
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Socially compliant path planning
Abhijat Biswas, Ting-Che Lin, and Sean Wang
[Report]
[Code]
[Video]
RTAA* + Social-LSTM based social navigation
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Automatic Extrinsic Calibration of Stereo Camera and 3D LiDAR
Abhijat Biswas, Aashi Manglik
[Poster]
We implement a method for estimation of MAV poses and dynamic parameters during flight.
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