Research Interests
I want to build ML systems which work well in data and resource constrained situations.
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Multi-Task Learning for Temporal Processes: A Case Study on Modeling Plant Cold Hardiness
Aseem Saxena, Paola Pesantez-Cabrera, Jonathan Magby, Markus Keller, Alan Fern
Machine Learning Journal, Springer, 2024 (Under Review)
We present a real-world case study of multi-task learning (MTL) for temporal process modeling from limited data. Specifically, we investigate multi-task learning for the important agricultural problem of predicting grape and cherry cold hardiness. We investigate multi-task learning (MTL) approaches for combining data, where different tasks correspond to different cultivars. Our results show significant differences between architectures and that certain architectures are able to consistently outperform single-task learning and state-of-the-art scientific models.
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Multi-Task Learning for Budbreak Prediction
Aseem Saxena, Paola Pesantez-Cabrera, Rohan Ballapragada, Markus Keller, Alan Fern
Workshop on AI for Agriculture and Food Systems at AAAI, 2023 (Accepted)
Grapevine budbreak is a key phenological stage of seasonal development, which serves as a signal
for the onset of active growth. This is also when grape plants are most vulnerable to damage from
freezing temperatures. Hence, it is important for winegrowers to anticipate the day of budbreak
occurrence to protect their vineyards from late spring frost events. This work investigates deep
learning for budbreak prediction using data collected for multiple grape cultivars. While some
cultivars have over 30 seasons of data others have as little as 4 seasons, which can adversely
impact prediction accuracy. To address this issue, we investigate multi-task learning, which
combines data across all cultivars to make predictions for individual cultivars. Our main result
shows that several variants of multi-task learning are all able to significantly improve prediction
accuracy compared to learning for each cultivar independently.
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Grape Cold Hardiness Prediction via Multi-Task Learning
Aseem Saxena, Paola Pesantez-Cabrera, Rohan Ballapragada, Kin-Ho Lam, Alan Fern, Markus
Keller
Innovative Applications of Artificial Intelligence (IAAI), 2023 (Accepted)
Cold temperatures during fall and spring have the potential to cause frost damage to
grapevines and other fruit plants, which can significantly decrease harvest yields. we study
whether deep-learning models can improve cold hardiness prediction for grapes based on data that
has been collected over a 30-year time period. A key challenge is that the amount of data per
cultivar is highly variable, with some cultivars having only a small amount. For this purpose,
we investigate the use of multi-task learning to leverage data across cultivars in order to
improve prediction performance for individual cultivars.
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Formalizing the Problem of Side Effect Regularization
Alexander Matt Turner, Aseem Saxena, Prasad Tadepalli
Equal Contribution
NeurIPS ML Safety Workshop, 2022 (Accepted)
AI objectives are often hard to specify properly. Some approaches tackle this problem by
regularizing the AI's side effects: Agents must weigh off" how much of a mess they make"
with an imperfectly specified proxy objective. We propose a formal criterion for side effect
regularization via the assistance game framework.
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Sim-to-Real Learning of Footstep-Constrained Bipedal Dynamic Walking
Helei Duan, Ashish Malik, Jeremy Dao, Aseem Saxena, Kevin Green, Jonah Siekmann, Alan
Fern, Jonathan Hurst
IEEE ICRA (International Conference on Robotics and Automation), 2022
(Accepted)
we aim to maintain the robust and dynamic nature of learned gaits while also
respecting footstep constraints imposed externally. We develop an RL formulation for
training dynamic gait controllers that can respond to specified touchdown locations. We
then successfully demonstrate simulation and sim-to-real performance on the bipedal
robot Cassie.
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LeTS-Drive: Driving in a Crowd by Learning from Tree Search
Panpan Cai, Yuanfu Luo, Aseem Saxena, David Hsu, Wee Sun Lee
RSS (Robotics Science and Systems), 2019 (Accepted)
video
Autonomous driving in a crowded environment, e.g., a busy traffic intersection, is
an unsolved challenge for robotics. We propose
LeTS-Drive, which integrates online POMDP planning and deep
learning.
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Exploring Convolutional Networks for End-to-End Visual Servoing
Aseem Saxena, Harit Pandya, Gourav Kumar, K.
Madhava Krishna
Equal Contribution
IEEE ICRA (International Conference on Robotics and Automation), 2017
(Accepted)
video
code
We present an end-to-end learning based approach for visual servoing in
diverse scenes where the knowledge of camera parameters and scene geometry is
not available apriori. This is achieved by training a convolutional neural
network over color images with synchronised camera poses.
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Avoiding Side
Effects in Complex
Navigation Environments
We explore methods to train agents to
complete tasks and simultaneously
avoid
side effects in the SafeLife. We
demonstrate the effectiveness of
MT-DQN, a
multi task variant of Deep Q
Networks for side effect avoidance.
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Distributed Q-Learning
We implement a distributed version of DQN via the Ray Distributed Framework.
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Offline-RL for Bipedal Robots
Reinforcement Learning requires the entire model of the world or interactive access to the world.
However, the world model may not be always known or it may be expensive or unsafe to perform
multiple interactions with the world. In such scenarios, we would like to make use of existing
transaction data to learn a control policy. This is addressed by a class of algorithms referred to
as "Offline Reinforcement Learning". In this work, we study and implement "Behaviour Cloning"(BC),
"TD3" and a combination of both "TD3+BC" for offline reinforcement learning. We evaluate them on
various syntheic datasets and investigate the performance of each of them on different qualities of
datasets. We also attempt to use offline RL for the real-world bipedal robot "Cassie" and introduce
various datasets for a bipedal locomotion task
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Studying Robustness of Semi-supervised Visual Features to Adversarial Attacks
Neural Network Verification is an important tool towards gauging robustness to adversaries. In this
report, I summarise the work of Salman et al who formulate most past work on LP based neural network
verification as a convex relaxation problem. The framework can handle different activation functions
and pooling layers and also can handle both primal and dual versions of verification. In my work, I
try to evaluate the adversarial robustness of classifiers which are trained to simultaneously
classify as well as reconstruct the input. I focus on two domains, image classification on the
CIFAR10 dataset and Q-Learning in the OpenAI gym cartpole environment.
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MC Dropout for Efficient Exploration
Agents need to explore the world intelligently so as to discover new skills that are useful to
perform downstream tasks. To perform exploration, there have been several methods that
have been introduced in literature – however they lack a one-on-one comparison under the same policy
setting. There is a discrepancy in terms of whether a model-based or a model-free policy is used to
perform exploration and the choice of policy can effect the sample-efficiency of the agent
significantly. In this project, we focus on implementing three exploration methods in model-based
reinforcement learning setting and thoroughly investigate their qualitative and quantitative
performance on the continuous control problem of Point Maze. Our experiments show that while
ensemble based Plan2Explore (Sekar et al. 2020) performs the best, a naive and simple method such as
Monte Carlo Dropout can perform on par with other exploration based methods.
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Motion Planning for Bipedal Robots
Using Sim2Real reinforcement learning, Siekmann et all has been able to demonstrate robust bipedal gaits such as standing, walking, hopping, running etc on bipedal robot Cassie. However, real world applications require robot autonomy above the level of heading velocity and direction to achieve meaningful goals. Motion and path planning using learned behaviors is still an open area of research in Robotics due to the computational requirements of planning algorithms and rapid updates required for real world applications. We tackle this problem for the Cassie Robot on simulated terrains using the RRT* algorithm to rapidly and reliable generate trajectory waypoints to reach the desired goals. These waypoints and Cassie’s real-time pose are used by a controller to set the policy targets which performs the lower level locomotion behavior. We successfully demonstrate our system in simulation in a number of challenging scenarios.
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Visualizing QMDPNet
I created a full fledged GUI Visualizer using Python Tkinter Library to understand the QMDPnet
algorithm. I
visualize various components of a POMDP such as reward map, belief and value function to get an
intuition on
how the algorithm works.
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Deep Learning for Table Interest Point Detection
I attempt to find interest points or corner points of tables in a scene using cues from
semantic segmentation and vanishing lines. Availabilty of semantic information such as
interest points can help mobile robots navigate in a better way.
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Automating GrabCut for Multilabel Image Segmentation
Performing Image Segmentation for 3 labels without user guidance by learning a GMM
for each label and performing alpha expansion algorithm using MRF2.2 Library.
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