(Nov 2023) Started a new 3-part blog post series (part 1) on the Lessons from AlphaZero which presents unique geometric interpretations of popular RL algorithms used in AlphaZero.
(Apr 2023) Our paper on a unified objective for model-based RL was accepted at ICML 2023! Excited to present the work at Hawaii this summer.
(Mar 2023) New preprint on a unified objective for model-based RL is online! Code is also available.
(Apr 2022) I am starting as a Senior Software Engineer at Aurora. Super excited to be part of the motion planning and machine learning team!
(Mar 2022) Our paper on the effectiveness of Iterative Learning Control over certainty equivalent control was accepted at L4DC 2022!
(Feb 2022) I passed my PhD Thesis Defense! Thanks to my committee Max, Drew, Oliver, and Leslie for being part of my journey. You can watch my defense here, and access the thesis here.
(Jan 2022) Our paper on improving soft duplicity detection in search based motion planning is accepted at ICRA 2022!
(Nov 2021) I will be defending my PhD on February 2nd 2022. The thesis will be available soon.
(May 2021) I will be working at the Apple Special Projects Group on something incredibly cool over the summer!
(Nov 2020) Our paper on integrating model-free learning with model-based planning to deal with inaccurate models has been accepted at AAAI 2021
(Nov 2020) Successfully passed my thesis proposal! On track to defend my thesis next year.
(Oct 2020) New blog post on my latest work on leveraging experience in planning with inaccurate models
(Sep 2020) New preprint on how to leverage experience in planning and execution using inaccurate models without ever updating the model is online! Code is also available.
(July 2020) My paper on a fast efficient solver for trajectory optimization with non-differentiable cost functions has been accepted at CDC 2020! Code is also released on github.
(May 2020) New blog post on my RSS 2020 paper with additional insights and connections to recent related work
(May 2020) Our paper on using inaccurate models for provably complete planning and execution has been accepted at RSS 2020! Code is also released.
(Apr 2020) Our latest preprint on a fast efficient solver for trajectory optimization with non-differentiable cost functions is online!
(Mar 2020) Our latest preprint on how to use inaccurate models for interleaving planning and execution efficiently is online!
(Jan 2020) Our systems paper on my PhD work in automated truck unloading has been accepted at ICRA 2020!
(Oct 2019) I was awarded the CMU Presidential Fellowship endowed by TCS to support my graduate tuition and stipend
(May 2019) My PhD work, in collaboration with NREC and Honeywell, has been covered by Bloomberg in their article on automating truck unloading!
(Apr 2019) Our paper on imitation learning from observations alone (without access to expert’s actions) with provable guarantees has been accepted at ICML 2019 for an Oral Presentation! Reproducible code is also released.
(Apr 2019) New blog post about interesting work that I got to see at AISTATS 2019
(Mar 2019) I will be at AISTATS 2019 happening in Okinawa, Japan during April 16-18 2019, presenting a poster on our work
(Jan 2019) I was awarded the CMU PhD Fellowship, 2019
(Jan 2019) I will be working with Vladlen Koltun at the Intelligent Systems Lab, Intel, Santa Clara over the summer of 2019
(Dec 2018) Our paper on why random search beats state-of-the-art policy gradient approaches got accepted at AISTATS 2019
(Nov 2018) A preliminary version of our paper is being presented at the Deep RL Workshop, NeurIPS 2018
(Jun 2018) My paper on regret analysis for simple exploration methods in contextual bandit problems is being presented at the Learning and Inference in Robotics workshop at RSS 2018
(May 2018) My paper on modeling attention in dense human crowds won the Best Paper in Cognitive Robotics award at ICRA 2018
(Jan 2018) Our paper on using structured recurrent models to obtain accurate future trajectory predictions got accepted at ICRA 2018
(Aug 2017) I will be starting my PhD in Robotics at RI, CMU working with Maxim Likhachev and Drew Bagnell on research areas at the intersection of planning and learning.
(Jul 2017) I graduated with a MS in Robotics from RI, CMU. Check out my thesis
(Feb 2017) I received the Max Planck ETH Center for Learning Systems PhD fellowship
(Jan 2017) Our paper on modeling cooperative navigation in dense human crowds using Gaussian Processes got accepted at ICRA 2017