corrections in papers added

_data/pubs/2024.yml

@@ -34,11 +34,11 @@
   id: mkrishna
   featured: 1
 
-- title: "Anticipate & Collab: Data-driven Task Anticipation and Knowledge-driven Planning for Human-robot Collaboration"
-  authors: Shivam Singh, Karthik Swaminathan, Raghav Arora, Ramandeep Singh, Ahana Datta, Dipanjan Das, Snehasis Banerjee, Mohan Sridharan, Madhava Krishna
+- title: "Anticipate & Act: Integrating LLMs and Classical Planning for Efficient Task Execution in Household Environments"
+  authors: Raghav Arora, Shivam Singh, Karthik Swaminathan, Ahana Datta, Snehasis Banerjee, Brojeshwar Bhowmick, Krishna Murthy Jatavallabhula, Mohan Sridharan, Madhava Krishna
   venue: ICRA 2024
   link: # https://arxiv.org/pdf/2404.03587
-    url: /publications/2024/Shivam_Anticipate
+    url: /publications/2024/Raghav_Anticipate
     display: Project Page
   id: mkrishna
   featured: 1

_pages/project-pages/2022/Vivek_Multi-Modal.md

@@ -24,7 +24,7 @@ affiliations:
 permalink: /publications/2022/Vivek_Multi-Modal/
 abstract: "Standard Model Predictive Control (MPC) or trajectory optimization approaches perform only a local search to solve a complex non-convex optimization problem. As a result, they cannot capture the multi-modal characteristic of human driving. A global optimizer can be a potential solution but is computationally intractable in a real-time setting. In this letter, we present a real- time MPC capable of searching over different driving modalities. Our basic idea is simple: we run several goal-directed parallel trajectory optimizations and score the resulting trajectories based on user-defined meta cost functions. This allows us to perform a search over several locally optimal motion plans. Although concep-tually straightforward, realizing this idea in real-time with existing optimizers is highly challenging from technical and computational standpoints. With this motivation, we present a novel batch non-holonomic trajectory optimization whose underlying matrix algebra is easily parallelizable across problem instances and reduces to computing large batch matrix-vector products. This structure, in turn, is achieved by deriving a linearization-free multi-convex reformulation of the non-holonomic kinematics and collision avoid-ance constraints. We extensively validate our approach using both synthetic and real data sets(NGSIM) of traffic scenarios. We highlight how our algorithm automatically takes lane-change and overtaking decisions based on the defined meta cost function. Our batch optimizer achieves trajectories with lower meta cost, up to 6x faster than competing baselines."
 paper: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9705556
-#code: https://github.com/sudarshan-s-harithas/CCO-VOXEL 
+code: https://github.com/vivek-uka/Batch-Opt-Highway-Driving/tree/master
 #supplement: https://iiitaphyd-my.sharepoint.com/personal/avneesh_mishra_research_iiit_ac_in/Documents/Forms/All.aspx?RootFolder=%2Fpersonal%2Favneesh%5Fmishra%5Fresearch%5Fiiit%5Fac%5Fin%2FDocuments%2FRRC%2FOpposing%20View%20Loop%20Closure%2FE2CNN%2FPresented%20Material%2FReF%20Paper&FolderCTID=0x012000A1AB309DA2EB7542856220193D0C0808
 #video: https://robotics.iiit.ac.in/publications/2020/deep-mpc-for-visual-servoing/video.mp4
 #iframe: https://www.youtube.com/embed/qNAqAlb7m3E # https://www.youtube.com/embed/jhjskX4FQwA

_pages/project-pages/2024/Nikhil_AnyLoc.md

@@ -35,7 +35,7 @@ project_page: https://anyloc.github.io/
 paper: https://arxiv.org/pdf/2308.00688.pdf
 code: https://github.com/AnyLoc/AnyLoc
 #supplement: https://clipgraphs.github.io/static/pdfs/Supplementary.pdf
-video: https://www.youtube.com/watch?v=ITo8rMInatk&feature=youtu.be
+# video: https://www.youtube.com/watch?v=ITo8rMInatk&feature=youtu.be
 iframe: https://www.youtube.com/embed/ITo8rMInatk
 demo: https://anyloc.github.io/#interactive_demo
 

_pages/project-pages/2024/Raghav_Anticipate.md

@@ -0,0 +1,46 @@
+---
+layout: project-page-new
+title: "Anticipate & Act: Integrating LLMs and Classical Planning for Efficient Task Execution in Household Environments"
+authors:
+  - name: Raghav Arora*
+    sup: 1
+  - name: Shivam Singh∗
+    sup: 1
+  - name: Karthik Swaminathan∗
+    sup: 1
+  - name: Ahana Dutta
+    sup: 1
+  - name: Snehasis Banerjee
+    sup: 2
+  - name: Brojeshwar Bhowmick
+    sup: 2
+  - name: Krishna Murthy Jatavallabhula
+    sup: 3
+  - name: Mohan Sridharan
+    sup: 4
+  - name: Madhava Krishna
+    sup: 1
+affiliations:
+  - name: Robotics Research Center, IIIT Hyderabad, India
+    link: https://robotics.iiit.ac.in
+    sup: 1
+  - name: TCS Research, Tata Consultancy Services, India
+    link: https://www.tcs.com/
+    sup: 2
+  - name: CSAIL, Massachusetts Institute of Technology, USA
+    link: https://www.csail.mit.edu/
+    sup: 3
+  - name: School of Informatics, University of Edinburgh, UK
+    link: https://informatics.ed.ac.uk/
+    sup: 4
+permalink: /publications/2024/Raghav_Anticipate/
+abstract: "Assistive agents performing household tasks such as making the bed, preparing coffee, or cooking breakfast, often consider one task at a time by computing a plan of actions that accomplishes this task. The agents can be more efficient by anticipating upcoming tasks, and computing and executing an action sequence that jointly achieves these tasks. State of the art methods for task anticipating use data-driven deep network architectures and Large Language Models (LLMs) for task estimation but they do so at the level of high-level tasks and/or require a large number of training examples. Our framework leverages the generic knowledge of LLMs through a small number of prompts to perform high-level task anticipation, using the anticipated tasks as joint goals in a classical planning system to compute a sequence of finer-granularity actions that jointly achieve these goals. We ground and evaluate our frameworks capabilities in realistic simulated scenarios in the VirtualHome environment and demonstrate a 31% reduction in the execution time in comparison with a system that does not consider upcoming tasks"
+project_page: https://raraghavarora.github.io/ahsoka/
+paper: https://events.infovaya.com/uploads/documents/pdfviewer/37/ff/133990-3210.pdf
+code: https://github.com/AnticipateAndAct/AnticipateAndAct/
+supplement: https://raraghavarora.github.io/PDFs/Anticipate_Act__Supplementary_Material.pdf
+#video: https://www.youtube.com/watch?v=QW5VCDIgXus
+iframe: https://www.youtube.com/watch?v=Q6V-8bXk8lA
+#demo: https://anyloc.github.io/#interactive_demo
+
+---