Dhiraj Gandhi
Email:dhirajgandhi[AT]fb.com
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Research and Selected Projects
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Object Goal Navigation using Goal-Oriented Semantic Exploration
Devendra
Chaplot , Dhiraj Gandhi, Abhinav Gupta, Ruslan
Salakhutdinov
NeurIPS 2020
Paper, Project page
This work studies the problem of object goal navigation which
involves navigating to an instance of the given object category in unseen
environments. We propose a modular system called, 'Goal-Oriented Semantic
Exploration' which builds an episodic semantic map and uses it to explore the
environment efficiently based on the goal object category. Empirical results in
visually realistic simulation environments show that the proposed model outperforms
a wide range of baselines including end-to-end learning-based methods as well as
modular map-based methods and led to the winning entry of the CVPR-2020 Habitat
ObjectNav Challenge.
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Visual Imitation Made Easy
Sarah Young , Dhiraj Gandhi, Shubham Tulsiani, Abhinav Gupta, Pieter
Abbeel, Lerrel
Pinto
CoRL 2020
Paper, Project page
Visual imitation learning provides a framework for learning complex
manipulation behaviors by leveraging human demonstrations. However, current
interfaces for imitation such as kinesthetic teaching or virtual reality based
control prohibitively restricts our ability to collect large-scale data in the wild.
Obtaining such diverse demonstration data is paramount for the generalization of
learned skills to novel, previously unseen scenarios. In this work, we present an
alternate interface for imitation that simplifies the data collection process, while
allowing for easy transfer to robots.
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Leaning to Explore using Active Neural SLAM
Devendra
Chaplot , Dhiraj Gandhi, Saurabh Gupta, Abhinav Gupta,
Ruslan
Salakhutdinov
ICLR 2020
Paper, Project page
This work presents a modular and hierarchical approach to learn
policies for exploring 3D environments, called Active Neural SLAM. Our approach
leverages the strengths of both classical and learning-based methods, by using
analytical path planners with learned SLAM module, and global and local policies.The
proposed model can also be easily transferred to the PointGoal task and was the
winning entry of CVPR 2019 Habitat PointGoal Navigation Challenge.
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Object-centric Forward Modeling for Model Predictive Control
Yufei Ye , Dhiraj
Gandhi, Abhinav Gupta, Shubham Tulsiani
CoRL 2019
arXiv, project page
In this paper we present an approach to learn an object-centric
forward model, and show that this allows us to plan for sequences of actions to
achieve distant desired goals. We propose to model a scene as a collection of
objects, each with an explicit spatial location and implicit visual feature, and
learn to model the effects of actions using random interaction data.
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PyRobot: An Open-source Robotics Framework for Research and
Benchmarking
Adithya Murali*,Adithya Murali*, Kalyan Vasudev Alwala*, Dhiraj Gandhi*, Lerrel Pinto, Saurabh Gupta, Abhinav Gupta [*
Equal contribution]
arXiv, project page, code, facebook AI blog
This paper introduces PyRobot, an open-source robotics framework for
research and benchmarking. PyRobot is a light-weight, high-level interface on top of
ROS that provides a consistent set of hardware independent mid-level APIs to control
different robots. PyRobot abstracts away details about low-level controllers and
inter-process communication, and allows non-robotics researchers (ML, CV
researchers) to focus on building high-level AI applications.
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Self-Supervised Exploration via Disagreement
Deepak Pathak*,
Dhiraj Gandhi* ,Abhinav Gupta [* Equal
contribution]
ICML 2019
arXiv, project
page, code
In this paper, we propose a formulation for exploration inspired
by the work in active learning literature. Specifically, we train an ensemble of
dynamics models and incentivize the agent to explore such that the disagreement
of those ensembles is maximized. This allows the agent to learn skills by
exploring in a self-supervised manner without any external reward.
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Robot Learning in Homes: Improving Generalization and Reducing
Dataset Bias
Abhinav Gupta*, Adithya Murali*, Dhiraj
Gandhi* , Lerrel
Pinto* [*Equal contribution]
NIPS2018
arXiv, data-set
We present the first systematic effort in collecting a large dataset for robotic
grasping in homes. The models trained with our home dataset showed a marked
improvement of 43.7% over a baseline model trained with data collected in lab.
Our architecture which explicitly models the latent noise in the dataset also
performed 10% better than one that did not factor out the noise.
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Learning to Grasp Without Seeing
Adithyavairavan Murali, Yin Li, Dhiraj Gandhi, Abhinav Gupta
ISER2018
arXiv, video, dataset,
project
page
Can a robot grasp an unknown object without seeing it? In this paper, we present
a tactile-sensing based approach to this challenging problem of grasping novel
objects without prior knowledge of their location or physical properties. Our
key idea is to combine touch based object localization with tactile based
re-grasping. To train our learning models, we created a large-scale grasping
dataset, including more than 30 RGB frames and over 2.8 million tactile samples
from 7800 grasp interactions of 52 objects.
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CASSL: Curriculum Accelerated Self-Supervised Learning
Adithyavairavan Murali, Lerrel Pinto, Dhiraj
Gandhi, Abhinav
Gupta
ICRA, 2018
arXiv, video
Recent self-supervised learning approaches focus on using a few thousand data
points to learn policies for high-level, low-dimensional action spaces. However,
scaling this framework for high-dimensional control require either scaling up
the data collection efforts or using a clever sampling strategy for training. We
present a novel approach - Curriculum Accelerated Self-Supervised Learning
(CASSL) - to train policies that map visual information to high-level, higher-
dimensional action spaces.
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Learning to Fly by Crashing
Dhiraj Gandhi, Lerrel
Pinto , Abhinav Gupta
IROS, 2017
arXiv, video,
dataset
& trained model
How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid
obstacles? In this project, we propose to bite the bullet and collect a dataset
of crashes itself! We build a drone whose sole purpose is to crash into objects:
it samples naive trajectories and crashes into random objects. We crash our
drone around 11K times to create one of the biggest UAV crash dataset. We show
that simple self-supervised models learnt on this data is quite effective in
navigating the UAV even in extremely cluttered environments with dynamic
obstacles including humans.
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The Curious Robot: Learning Visual Representations via Physical
Interactions
Lerrel Pinto, Dhiraj
Gandhi, Yuanfeng Han, Yong-Lae Park and Abhinav Gupta
Spotlight Presentation at ECCV 2016
arXiv
What is the right supervisory signal to train visual representations? In case of
biological agents, visual representation learning does not require semantic
labels. In fact, we argue that biological agents use active exploration and
physical interactions with the world to learn visual representations unlike
current vision systems which just use passive observations (images and videos
downloaded from web). Towards this goal, we build one of the first systems on a
Baxter platform that pushes, pokes, grasps and actively observes objects in a
tabletop environment.
We use these physical interactions to collect more than 130K datapoints, with
each datapoint providing backprops to a shared ConvNet architecture allowing us
to learn visual representations. We show the quality of learned representations
by observing neuron activations and performing nearest neighbor retrieval on
this learned representation. Finally we evaluate our learned ConvNet on
different image classification tasks.
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Auto PID tuning of Automated Guided Vehicle(AGV)
Work experience at The Hi-Tech
Robotic Systemz Ltd.
video
Engineers at company faced difficulties in tuning the AGVs PID controller using
the prevalent Ziegler-Nichols
technique. This tuning technique was also not robust enough to work on
varying loading conditions (from 0-2000lb). To overcome this cumbersome process,
I undertook an initiative to automate it. After conducting a literature review
and running proof-of-concept experiments in the Gazebo simulator, I implemented
a
model free Iterative
Feedback Tuning scheme. AGVs tuned with this method are running smoothly
in production for the past couple of years at the Honda and Nokia plants in
India.
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Sampling based path planning algorithm
video, code
In this project I tried to study different sampling based algorithms like PRM,
RRT, RRT* and implemented them in matlab. I tried to smooth out trajectory
obtained using these algorithms using Bezier curve method. Furthermore I tried
to exted these algoritms for car like non-holonomic object using Dubins curve
and Reeds-Shepps curve.
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Design & Development of hybrid legged and wheeled robot
Dhiraj Gandhi, Jagjeet Singh
, A. Narayana Reddy
Proceedings of iNaCoMM2015
pdf,
video
Wheeled locomotion is most widely used mechanism for mobile robots on even
terrain whereas walking
mechanism is suitable for mobility of robots on uneven terrain. To achieve
advantages of
wheeled as well as biped robot, we designed a robot with both mobility
mechanisms. A 6-degree of
freedom (DoF) biped robot has been constructed with 3 DoF on each leg with
mechanism of transforming it into a wheeled robot.
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Design of Parrlel Planar Manipulator (PPM)
2014 summer internship at Systemantics
pdf,
video
Parallel manipulators have higher payload capacity, higher mechanical rigidity
and better
accuracy than their serial counter parts. However presence of singularities
within the workspace is major problem with these manipulators. In this project I
worked on optimizing the ratio of singularity free workspace to the total space
occupied by the mechanism with respect to link lenght. Furthermore I extended
the work to come up with design algorithm that engineers can use for its optimal
design. On application side I used 2RR PPM to deposit glue
evenly on engine gaskit in simulation
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Design & Development of Spherical Robot
Dhiraj Gandhi, Akshay Khatri and Leena Vachhani
2013 Summer internship at SysCon Dept. IIT
Bombay
pdf,
video
Spherical Robots have several advantages over robots that use wheels or legs for
locomotion,
including inner component protection, low rolling resistance, and ability to
move in any direction.
At the
same time, it brings number of challenging problems in modelling, stabilization,
and path following.
In this project we designed and manufactured novel two pendulum based Spherical
robot within diameter of 6 inch and achieved remote controlled omni-directional
movements. We also mathematically modeled the system to observe the effect of
pendulum state on the motion of robot.
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