website_2_0_1

Author: SasiAlavala

_data/publications.yml

@@ -43,18 +43,18 @@
 - id: v-ocbf
   title: "V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functions"
   authors: "M Tayal, M Tayal, A Singh, S Kolathaya, R Prakash"
-  venue: "Transactions on Machine Learning Research (TMLR)"
+  venue: "Transactions on Machine Learning Research"
   tag: "TMLR'26"
   year: 2026
   type: journal
   image: placeholder.png
   links:
-    - { label: Paper, url: "https://arxiv.org/abs/2512.10822" }
+    - { label: Paper, url: "https://openreview.net/forum?id=PGO9mpIyyb" }
 
 - id: st2
   title: "Sequentially Teaching Sequential Tasks (ST)<sup>2</sup>: Teaching Robots Long-Horizon Manipulation Skills"
   authors: "Z Ajanović, R Prakash, L de Souza Rosa, J Kober"
-  venue: "IEEE Robotics & Automation Magazine"
+  venue: "IEEE Robotics and Automation Magazine"
   tag: "RA-M'26"
   year: 2026
   type: journal
@@ -65,31 +65,30 @@
 - id: safedmps-icra26
   title: "SafeDMPs: Integrating Formal Safety with DMPs for Adaptive HRI"
   authors: "P Tiwari, S Nath, R Prakash"
-  venue: "2026 IEEE International Conference on Robotics and Automation (ICRA)"
+  venue: "IEEE International Conference on Robotics and Automation (ICRA)"
   tag: "ICRA'26"
   year: 2026
   type: conference
   image: safedmps_26.png
   links:
-    - { label: Paper, url: "https://arxiv.org/abs/2509.16482" }
+    - { label: Paper, url: "https://arxiv.org/abs/2603.29708" }
     - { label: Project Site, url: "https://tiwari-pranav.github.io/SafeDMPs/" }
-    - { label: Code, url: "https://github.com/Tiwari-Pranav/ghost-In-the-Arm/" }
 
 - id: variable-impedance-icra26
   title: "Safe and Optimal Variable Impedance Control via Certified Reinforcement Learning"
   authors: "S Kumar, R Prakash"
-  venue: "2026 IEEE International Conference on Robotics and Automation (ICRA)"
+  venue: "IEEE International Conference on Robotics and Automation (ICRA)"
   tag: "ICRA'26"
   year: 2026
   type: conference
   image: rlcgms_26.png
   links:
-    - { label: Paper }
+    - { label: Paper, url: "https://arxiv.org/abs/2511.16330" }
 
 - id: beyond-the-teacher-icra26
   title: "Beyond the Teacher: Leveraging Mixed-Skill Demonstrations for Robust Imitation Learning"
   authors: "S Saharsh, S Sonkar, P Jagtap, R Prakash"
-  venue: "2026 IEEE International Conference on Robotics and Automation (ICRA)"
+  venue: "IEEE International Conference on Robotics and Automation (ICRA)"
   tag: "ICRA'26"
   year: 2026
   type: conference
@@ -112,7 +111,7 @@
 
 - id: diffusionpack
   title: "DiffusionPack: Bin Packing with Custom Human Preferences"
-  authors: "A Maurya, S Vyas-b, A Bassunadhar, R Prakash"
+  authors: "A Maurya, S Vats, G Balachandran, R Prakash"
   venue: "NeurIPS 2025 Workshop: Bridging Language, Agent, and World Models for Robotics"
   tag: "NeurIPSW'25"
   year: 2025
@@ -131,6 +130,7 @@
   image: ovita_25.gif
   links:
     - { label: Paper, url: "https://ieeexplore.ieee.org/abstract/document/11150730" }
+    - { label: Code, url: "https://github.com/anurag1000101/OVITA" }
 
 - id: safedmps-corlw25
   title: "SafeDMPs: Integrating Formal Safety with DMPs for Adaptive HRI"
@@ -142,8 +142,6 @@
   image: corlw_saferol_25.png
   links:
     - { label: Paper, url: "https://openreview.net/forum?id=Xj3V96qTpf" }
-    - { label: Project Site, url: "https://tiwari-pranav.github.io/SafeDMPs/" }
-    - { label: Code, url: "https://github.com/Tiwari-Pranav/ghost-In-the-Arm/" }
 
 - id: beyond-the-teacher-corlw25
   title: "Beyond the Teacher: Leveraging Mixed-Skill Demonstrations for Robust Imitation Learning"
@@ -155,8 +153,6 @@
   image: corlw_data_25.png
   links:
     - { label: Paper, url: "https://openreview.net/forum?id=7Ox8aKIFbA" }
-    - { label: Project Site, url: "https://focaslab.github.io/beyondtheteacher/" }
-    - { label: Code, url: "https://github.com/FocasLab/ICRA-IL-2026" }
 
 - id: pacer
   title: "PACER: Progress-Aligned Curation for Error-Resilient Imitation Learning"
@@ -179,11 +175,12 @@
   image: tro_25.gif
   links:
     - { label: Paper, url: "https://ieeexplore.ieee.org/abstract/document/11049008" }
+    - { label: Code, url: "https://github.com/franzesegiovanni/policy_transportation" }
 
 - id: imphrl
   title: "Impedance Primitive-Augmented Hierarchical Reinforcement Learning for Sequential Tasks"
   authors: "AB Tahmaz, R Prakash, J Kober"
-  venue: "2025 IEEE International Conference on Robotics and Automation (ICRA)"
+  venue: "IEEE International Conference on Robotics and Automation (ICRA)"
   tag: "ICRA'25"
   year: 2025
   type: conference
@@ -205,7 +202,7 @@
 - id: hirl-workshop
   title: "3rd Workshop on Human-Interactive Robot Learning (HIRL)"
   authors: "M Racca, R Mirsky, E Senft, X Xiao, I Idrees, A Kshirsagar, R Prakash"
-  venue: "Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction"
+  venue: "ACM/IEEE International Conference on Human-Robot Interaction (HRI)"
   tag: "HRI'24"
   year: 2024
   type: workshop

_pages/contact.md

@@ -11,16 +11,16 @@ permalink: /contact/
     <div class="contact-section">
       <h2>Prospective Students</h2>
       <p>
-        We are always looking for highly motivated students interested in robotics, control theory, machine learning, and human-robot interaction. View our research at <a href="https://scholar.google.com/citations?user=mV2CW4QAAAAJ" target="_blank">Google Scholar</a>. If you are interested in joining our lab:
+        We are always looking for highly motivated students interested in robotics, control theory, machine learning, and human-robot interaction. If you are interested in joining our lab:
       </p>
       <ul>
-        <li>Postdoctoral researchers: Candidates with a background in robotics, control, or machine learning may join through fellowships such as the IoE, C V Raman, National Post-Doctoral, or DS Kothari fellowships. Please write to HiRo Lab with your CV and research interests.</li>
-        <li>PhD / MTech(R) applicants: Apply to the Department of Cyber-Physical Systems through the <a href="https://iisc.ac.in/admissions/" target="_blank">IISc admissions portal</a>. Applications are typically open in March–April and September–October.</li>
+        <li>Postdoctoral researchers: Candidates with a background in robotics, control, or machine learning may join through fellowships such as the C V Raman Post-Doctoral, National Post-Doctoral, or DS Kothari fellowships. Please write to <a href="mailto:hiro.cps.iisc@gmail.com">hiro.cps.iisc@gmail.com</a>, cc'ing Prof. Ravi Prakash with your CV and research interests.</li>
+        <li>PhD / MTech(R) applicants: Apply to the Department of Cyber-Physical Systems through the <a href="https://iisc.ac.in/admissions/" target="_blank">IISc admissions portal</a>. Applications are typically open in March–April and September–October. Please write to <a href="mailto:hiro.cps.iisc@gmail.com">hiro.cps.iisc@gmail.com</a>, cc'ing Prof. Ravi Prakash with your CV and research interests.</li>
         <li>Master's students (IISc): If you want to pursue your master's project with HiRo Lab, visit Prof. Ravi Prakash at his office or write to him.</li>
-        <li>Research staff and UG interns: Please email <a href="mailto:hiro.cps.iisc@gmail.com">hiro.cps.iisc@gmail.com</a> with your interests and CV. Undergraduate students are also encouraged to apply through programs such as the <a href="https://www.ias.ac.in/" target="_blank">INSA Summer Research Fellowship</a>, which are usually announced in October–November.</li>
+        <li>Research staff and UG interns: Please email <a href="mailto:hiro.cps.iisc@gmail.com">hiro.cps.iisc@gmail.com</a> with your interests and CV. UG students are also encouraged to apply through programs such as the <a href="https://www.ias.ac.in/" target="_blank">INSA Summer Research Fellowship</a>, which are usually announced in October–November. For UG interns, a minimum of 2 months must be spent here at the lab to receive an internship certificate, and most internships are self-funded.</li>
       </ul>
       <p>
-        When writing to HiRo Lab, please include a short note on your background, why our lab interests you (a specific project or research direction), the skills you would bring, and your CV.
+        When writing to HiRo Lab, please include a short note on your background, why HiRo lab interests you (a specific project or research direction), the skills you would bring, and your CV.
       </p>
       <p>
         <strong>Note:</strong> We receive a large number of applications. If you do not hear back within a week, please send a gentle reminder. If you still do not receive a response after that, your application has been reviewed but is not a fit for our current openings.

_pages/people.md

@@ -194,7 +194,7 @@ permalink: /people/
   </div>
 
   <h2>Robots</h2>
-  <div class="people-grid">
+  <div class="people-grid robots-grid">
     <div class="person-card">
       <a href="https://franka.de/franka-research-3" target="_blank" class="person-image-link">
         <img src="/assets/img/people/franka_research_3.jpg" alt="Franka Research 3"/>

_pages/research.md

@@ -13,10 +13,11 @@ permalink: /research/
 
   <div class="research-intro">
     <p>
-      Our mission is to develop theoretical foundations and practical algorithms for human-interactive robotics.
-      Our group focuses on enabling robots to learn from and collaborate with humans in real-world environments.
-      We leverage tools from machine learning, control theory, and human-robot interaction to build intelligent,
-      adaptive, and safe robotic systems.
+      Our mission is to build the theoretical foundations and practical algorithms that let robots learn from,
+      adapt to, and collaborate with people in unstructured, real-world environments. We draw on machine learning,
+      optimal control, and formal methods to make robotic systems data-efficient, generalizable, and provably safe
+      during physical human-robot interaction. The themes below summarize our current directions and representative
+      results.
     </p>
   </div>
 
@@ -25,13 +26,18 @@ permalink: /research/
       <div class="research-area-content">
         <h2>Interactive Reinforcement Learning</h2>
         <p>
-          We develop methods for robots to learn complex manipulation tasks through hierarchical reinforcement
-          learning and imitation learning. Our work on
-          <a href="/publications/#imphrl" class="work-link">Impedance Primitive-Augmented Hierarchical RL (ICRA'25)</a>
-          augments high-level RL policies with impedance control primitives to solve long-horizon sequential tasks,
-          enabling compliant and adaptive skill acquisition. We also investigate robust imitation learning that
-          exploits mixed-quality demonstrations to improve generalization from imperfect human teachers, as in
-          <a href="/publications/#beyond-the-teacher-icra26" class="work-link">Beyond the Teacher (ICRA'26)</a>.
+          Reinforcement learning (RL) and learning from demonstration let robots acquire manipulation skills directly
+          from interaction and human guidance. The hard cases are long-horizon, contact-rich tasks and learning
+          robustly from limited, imperfect human data. We address the first with
+          <a href="/publications/#imphrl" class="work-link">Impedance Primitive-Augmented Hierarchical RL (ICRA'25)</a>,
+          which gives a high-level RL policy a low-level action space of parameterized impedance-control primitives,
+          yielding compliant, contact-rich skills over extended task sequences. For the second, we make imitation
+          robust to the quality of the teacher:
+          <a href="/publications/#beyond-the-teacher-icra26" class="work-link">Beyond the Teacher (ICRA'26)</a>
+          leverages mixed-skill demonstrations to learn policies that tolerate inconsistent human teachers,
+          <a href="/publications/#rise" class="work-link">RISE (IROSW'25)</a> uses stochastic latent encodings for
+          robust imitation, and <a href="/publications/#pacer" class="work-link">PACER (CoRLW'25)</a> curates
+          demonstrations by task progress to curb the compounding errors that destabilize behavior cloning.
         </p>
       </div>
       <div class="research-area-image">
@@ -41,13 +47,16 @@ permalink: /research/
 
     <div class="research-area">
       <div class="research-area-content">
-        <h2>Foundational Models for Robotics</h2>
+        <h2>Foundation Models for Robotics</h2>
         <p>
-          We explore how large-scale pre-trained vision-language models can enable natural, flexible
-          robot-human interaction. Our work on
+          Foundation models, including large vision-language models (VLMs) and, increasingly, vision-language-action
+          (VLA) models pretrained on web- and robot-scale data, supply semantic priors and broad generalization that
+          can ground robot behavior in natural language and perception. We study how to turn these priors into
+          natural, flexible human-robot interaction. Our work on
           <a href="/publications/#ovita" class="work-link">OVITA (RA-L'25)</a>
-          uses vision-language models to adapt robot trajectories in real time based on open-vocabulary human
-          language instructions, providing interpretable trajectory modifications without retraining.
+          harnesses pretrained foundation models to adapt reference trajectories from open-vocabulary language
+          instructions at execution time, producing interpretable, parameterized edits to robot motion without any
+          task-specific retraining.
         </p>
       </div>
       <div class="research-area-image">
@@ -59,18 +68,19 @@ permalink: /research/
       <div class="research-area-content">
         <h2>Safe and Compliant Human-Robot Interaction</h2>
         <p>
-          Safety and compliance are essential when robots work alongside humans. Our work on
+          When robots make physical contact with people, behavior must be both compliant and provably safe, with
+          guarantees that hold during interaction and not just on average. We bring formal methods and
+          control-theoretic certificates together with learning to enforce this.
           <a href="/publications/#safedmps-icra26" class="work-link">SafeDMPs (ICRA'26)</a>
-          integrates Signal Temporal Logic (STL)-based formal safety specifications directly into Dynamic
-          Movement Primitives, enabling adaptive robot motions that are provably safe during physical human-robot
-          contact. We also develop
-          <a href="/publications/#variable-impedance-icra26" class="work-link">certified reinforcement learning for variable impedance control (ICRA'26)</a>,
-          which combines Lyapunov-based stability certificates with RL to achieve optimal, safe impedance modulation
-          during interaction. More recently, we study how safety can be learned directly from offline data:
+          embeds Signal Temporal Logic (STL) safety specifications directly into Dynamic Movement Primitives, so
+          that adaptive motions remain provably safe during physical human-robot contact, while
+          <a href="/publications/#variable-impedance-icra26" class="work-link">certified RL for variable impedance control (ICRA'26)</a>
+          pairs RL with Lyapunov-based stability certificates to modulate impedance optimally with guaranteed
+          stability. We also learn safety directly from offline data, without online interaction:
           <a href="/publications/#safe-flow-q-learning" class="work-link">Safe Flow Q-Learning (RLC'26)</a>
-          couples reachability analysis with flow-based policies for offline safe reinforcement learning, while
+          couples reachability analysis with flow-based policies for offline safe RL, and
           <a href="/publications/#v-ocbf" class="work-link">V-OCBF (TMLR'26)</a>
-          learns value-guided control barrier functions as safety filters from offline datasets.
+          learns value-guided control barrier functions that act as safety filters.
         </p>
       </div>
       <div class="research-area-image">
@@ -82,15 +92,15 @@ permalink: /research/
       <div class="research-area-content">
         <h2>Optimization and Optimal Control</h2>
         <p>
-          We develop optimization-based and data-driven approaches for robot motion planning and control.
-          Our <a href="/publications/#adaptive-critic" class="work-link">Adaptive Critic (T-CST'24)</a>
-          framework learns optimal controllers for uncertain robot manipulators using neural network-based value
-          function approximation, achieving data-efficient online adaptation without explicit system identification.
-          Our work on
-          <a href="/publications/#transportation-maps" class="work-link">generalizable motion policies through keypoint parameterization and transportation maps (T-RO'25)</a>
-          enables one-shot generalization of manipulation skills to novel object configurations. We also develop
-          <a href="/publications/#st2" class="work-link">ST² (RA-M'26)</a>, a framework for teaching robots
-          long-horizon manipulation skills through sequential human demonstrations.
+          Optimization and optimal control offer principled, data-efficient ways to synthesize controllers and motion
+          policies, often with stability or generalization guarantees. Our
+          <a href="/publications/#adaptive-critic" class="work-link">Adaptive Critic (T-CST'24)</a>
+          framework uses approximate dynamic programming with neural value-function approximation to learn
+          near-optimal controllers for uncertain manipulators online, without explicit system identification.
+          <a href="/publications/#transportation-maps" class="work-link">Keypoint parameterization with transportation (optimal-transport) maps (T-RO'25)</a>
+          yields motion policies that generalize one-shot to novel object configurations, and
+          <a href="/publications/#st2" class="work-link">ST² (RA-M'26)</a>
+          teaches robots long-horizon manipulation by composing skills from sequential human demonstrations.
         </p>
       </div>
       <div class="research-area-image">
@@ -102,12 +112,13 @@ permalink: /research/
       <div class="research-area-content">
         <h2>3D Vision, SLAM and World Models</h2>
         <p>
-          We develop methods for robots to build accurate 3D models of their environment through active perception,
-          including SLAM (Simultaneous Localization and Mapping), real-time scene reconstruction, world models for
-          spatial understanding, and object-level mapping integrated with manipulation planning. Our work on
-          <a href="/publications/#affordmatcher" class="work-link">AffordMatcher (CVPR'26)</a>
-          learns object affordances in 3D scenes from visual signifiers, connecting 3D scene understanding to
-          manipulation in complex, unstructured spaces.
+          Acting in unstructured environments demands spatial understanding from 3D reconstruction, simultaneous
+          localization and mapping (SLAM), and world models that capture both geometry and semantics. We are building
+          active-perception pipelines that tie real-time scene reconstruction and object-level mapping to
+          manipulation planning. A first step in this direction,
+          <a href="/publications/#affordmatcher" class="work-link">AffordMatcher (CVPR'26)</a>,
+          learns object affordances in 3D scenes from visual signifiers, connecting 3D scene understanding to where
+          and how a robot should act in complex, cluttered spaces.
         </p>
       </div>
       <div class="research-area-image">