Jackiw-Pi Model: A Superfield Approach

We derive the off-shell nilpotent and absolutely anticommuting Becchi-Rouet-Stora-Tyutin (BRST) as well as anti-BRST transformations s_{(a)b} corresponding to the Yang-Mills gauge transformations of 3D Jackiw-Pi model by exploiting the "augmented" superfield formalism. We also show that the Curci-Ferrari restriction, which is a hallmark of any non-Abelian 1-form gauge theories, emerges naturally within this formalism and plays an instrumental role in providing the proof of absolute anticommutativity of s_{(a)b}.Comment: LaTeX file, 6 pages, Talk delivered at SQS'2013 (BLTP, JINR, Dubna, Russia

Novel Symmetries in Vector Schwinger Model

We derive nilpotent and absolutely anticommuting (anti-)co-BRST symmetry transformations for the bosonized version of (1+1)-dimensional (2D) vector Schwinger model. These symmetry transformations turn out to be the analogue of co-exterior derivative of differential geometry as the total gauge-fixing term remains invariant under it. The exterior derivative is realized in terms of the (anti-)BRST symmetry transformations of the theory whereas the bosonic symmetries find their analogue in the Laplacian operator. The algebra obeyed by these symmetry transformations turns out to be exactly same as the algebra obeyed by the de Rham cohomological operators of differential geometry.Comment: LaTeX file, 12+1 pages, no figures, typos fixed, references expanded, text modified, version to appear in MPL

Learning Exploration Policies for Navigation

Numerous past works have tackled the problem of task-driven navigation. But, how to effectively explore a new environment to enable a variety of down-stream tasks has received much less attention. In this work, we study how agents can autonomously explore realistic and complex 3D environments without the context of task-rewards. We propose a learning-based approach and investigate different policy architectures, reward functions, and training paradigms. We find that the use of policies with spatial memory that are bootstrapped with imitation learning and finally finetuned with coverage rewards derived purely from on-board sensors can be effective at exploring novel environments. We show that our learned exploration policies can explore better than classical approaches based on geometry alone and generic learning-based exploration techniques. Finally, we also show how such task-agnostic exploration can be used for down-stream tasks. Code and Videos are available at: https://sites.google.com/view/exploration-for-nav

Visual Semantic Role Labeling

In this paper we introduce the problem of Visual Semantic Role Labeling: given an image we want to detect people doing actions and localize the objects of interaction. Classical approaches to action recognition either study the task of action classification at the image or video clip level or at best produce a bounding box around the person doing the action. We believe such an output is inadequate and a complete understanding can only come when we are able to associate objects in the scene to the different semantic roles of the action. To enable progress towards this goal, we annotate a dataset of 16K people instances in 10K images with actions they are doing and associate objects in the scene with different semantic roles for each action. Finally, we provide a set of baseline algorithms for this task and analyze error modes providing directions for future work

Nilpotent Symmetries in Jackiw-Pi Model: Augmented Superfield Approach

We derive the complete set of off-shell nilpotent (s^2_{(a)b} = 0) and absolutely anticommuting (s_b s_{ab} + s_{ab} s_b = 0) Becchi-Rouet-Stora-Tyutin (BRST) (s_b) as well as anti-BRST symmetry transformations (s_{ab}) corresponding to the combined Yang-Mills and non-Yang-Mills symmetries of the (2 + 1)-dimensional Jackiw-Pi model within the framework of augmented superfield formalism. The absolute anticommutativity of the (anti-)BRST symmetries is ensured by the existence of two sets of Curci-Ferrari (CF) type of conditions which emerge naturally in this formalism. The presence of CF conditions enables us to derive the coupled but equivalent Lagrangian densities. We also capture the (anti-)BRST invariance of the coupled Lagrangian densities in the superfield formalism. The derivation of the (anti-)BRST transformations of the auxiliary field \rho is one of the key findings which can neither be generated by the nilpotent (anti-)BRST charges nor by the requirements of the nilpotency and/or absolute anticommutativity of the (anti-)BRST transformations. Finally, we provide a bird's-eye view on the role of auxiliary field for various massive models and point out few striking similarities and some glaring differences among them.Comment: LaTex file: 24 pages, no figures, minor modifications in the title and text, references expanded, version to appear in IJT

Augmented Superfield Approach to Non-Yang-Mills Symmetries of Jackiw-Pi Model: Novel Observations

We derive the off-shell nilpotent and absolutely anticommuting Becchi-Rouet-Stora-Tyutin (BRST) as well as anti-BRST symmetry transformations corresponding to the non-Yang-Mills symmetry transformations of (2 + 1)- dimensional Jackiw-Pi (JP) model within the framework of "augmented" superfield formalism. The Curci-Ferrari restriction, which is a hallmark of non-Abelian 1-form gauge theories, does not appear in this case. One of the novel features of our present investigation is the derivation of proper (anti-)BRST symmetry transformations corresponding to the auxiliary field \rho that can not be derived by any conventional means.Comment: LaTeX file, 17 pages, journal version, typos fixed, references modifie

Anti Self-Dual Yang-Mills, Modified Faddeev-Jackiw Formalism and Hidden BRS Invariance

We analyze the constraints for a system of anti self-dual Yang-Mills (ASDYM) equations by means of the modified Faddeev-Jackiw method in K and J gauges \`{a} la Yang. We also establish the Hamiltonian flow for ASDYM system through the hidden BRS invariance in both the gauges. Finally, we remark on the bi-Hamiltonian nature of ASDYM and the compatibility of the symplectic structures therein.Comment: Typos fixed, revised version accepted for publicatio

Canonical brackets from continuous symmetries: Abelian 2-form gauge theory

We derive the canonical (anti-)commutation relations amongst the creation and annihilation operators of the various basic fields, present in the four (3 + 1)-dimensional (4D) free Abelian 2-from gauge theory, with the help of continuous symmetry transformations within the framework of Becchi-Rouet-Stora-Tyutin (BRST) formalism. We show that all the six continuous symmetries of the theory lead to the exactly the same non-vanishing (anti-)commutator amongst the creation and annihilation operators of the normal mode expansion of the basic fields of the theory.Comment: LaTeX file, 16 pages, No figure

Exploring Person Context and Local Scene Context for Object Detection

In this paper we explore two ways of using context for object detection. The first model focusses on people and the objects they commonly interact with, such as fashion and sports accessories. The second model considers more general object detection and uses the spatial relationships between objects and between objects and scenes. Our models are able to capture precise spatial relationships between the context and the object of interest, and make effective use of the appearance of the contextual region. On the newly released COCO dataset, our models provide relative improvements of up to 5% over CNN-based state-of-the-art detectors, with the gains concentrated on hard cases such as small objects (10% relative improvement)

Learning Navigation Subroutines from Egocentric Videos

Planning at a higher level of abstraction instead of low level torques improves the sample efficiency in reinforcement learning, and computational efficiency in classical planning. We propose a method to learn such hierarchical abstractions, or subroutines from egocentric video data of experts performing tasks. We learn a self-supervised inverse model on small amounts of random interaction data to pseudo-label the expert egocentric videos with agent actions. Visuomotor subroutines are acquired from these pseudo-labeled videos by learning a latent intent-conditioned policy that predicts the inferred pseudo-actions from the corresponding image observations. We demonstrate our proposed approach in context of navigation, and show that we can successfully learn consistent and diverse visuomotor subroutines from passive egocentric videos. We demonstrate the utility of our acquired visuomotor subroutines by using them as is for exploration, and as sub-policies in a hierarchical RL framework for reaching point goals and semantic goals. We also demonstrate behavior of our subroutines in the real world, by deploying them on a real robotic platform. Project website: https://ashishkumar1993.github.io/subroutines/