updated driving_object_importance_icra25

_data/pubs.yml

@@ -15,6 +15,28 @@
 # Current tags for research areas, see the research areas page for more details:
 # deform_obj_manip, 3D_afford_obj_manip, multimodal, rl_algs, auto_driving, active_perception, self_sup_rob
 
+- abs: The ability to identify important objects in a complex and dynamic driving environment is essential for autonomous driving agents to make safe and efficient driving decisions. It also helps assistive driving systems decide when to alert drivers. We tackle object importance estimation in a data-driven fashion and introduce HOIST - Human-annotated Object Importance in Simulated Traffic. HOIST contains driving scenarios with human-annotated importance labels for vehicles and pedestrians. We additionally propose a novel approach that relies on counterfactual reasoning to estimate an object's importance. We generate counterfactual scenarios by modifying the motion of objects and ascribe importance based on how the modifications affect the ego vehicle's driving. Our approach outperforms strong baselines for the task of object importance estimation on HOIST. We also perform ablation studies to justify our design choices and show the significance of the different components of our proposed approach.
+  authors: Pranay Gupta, Abhijat Biswas, Henny Admoni, David Held
+  award: null
+  bib: >
+    @article{gupta2023object,
+      title={Object Importance Estimation using Counterfactual Reasoning for Intelligent Driving},
+      author={Gupta, Pranay and Biswas, Abhijat and Admoni, Henny and Held, David},
+      journal={arXiv preprint arXiv:2312.02467},
+      year={2023}
+    }
+  img: ../pics/gupta2023object.png
+  links: 
+      '[arXiv]': https://arxiv.org/abs/2312.02467
+      '[Code]': https://github.com/vehicle-importance/oiecr
+  short_id: gupta2023object
+  site: https://vehicle-importance.github.io/
+  title: "Object Importance Estimation using Counterfactual Reasoning for Intelligent Driving"
+  venue: Robotics and Automation Letters (RAL), 2024 with presentation at the International Conference on Robotics and Automation (ICRA), 2025
+  video_embed: null
+  tags: 
+      - auto_driving
+
 - abs: "Sim2Real transfer, particularly for manipulation policies relying on RGB images, remains a critical challenge in robotics due to the significant domain shift between synthetic and real-world visual data. In this paper, we propose SplatSim, a novel framework that leverages Gaussian Splatting as the primary rendering primitive to reduce the Sim2Real gap for RGB-based manipulation policies. By replacing traditional mesh representations with Gaussian Splats in simulators, SplatSim produces highly photorealistic synthetic data while maintaining the scalability and cost-efficiency of simulation. We demonstrate the effectiveness of our framework by training manipulation policies within SplatSim and deploying them in the real world in a zero-shot manner, achieving an average success rate of 86.25%, compared to 97.5% for policies trained on real-world data."
   authors: M. Nomaan Qureshi, Sparsh Garg, Francisco Yandun, David Held, George Kantor, Abhisesh Silwal
   bib: >
@@ -212,28 +234,6 @@
     - deformable
     - multimodal
 
-- abs: The ability to identify important objects in a complex and dynamic driving environment is essential for autonomous driving agents to make safe and efficient driving decisions. It also helps assistive driving systems decide when to alert drivers. We tackle object importance estimation in a data-driven fashion and introduce HOIST - Human-annotated Object Importance in Simulated Traffic. HOIST contains driving scenarios with human-annotated importance labels for vehicles and pedestrians. We additionally propose a novel approach that relies on counterfactual reasoning to estimate an object's importance. We generate counterfactual scenarios by modifying the motion of objects and ascribe importance based on how the modifications affect the ego vehicle's driving. Our approach outperforms strong baselines for the task of object importance estimation on HOIST. We also perform ablation studies to justify our design choices and show the significance of the different components of our proposed approach.
-  authors: Pranay Gupta, Abhijat Biswas, Henny Admoni, David Held
-  award: null
-  bib: >
-    @article{gupta2023object,
-      title={Object Importance Estimation using Counterfactual Reasoning for Intelligent Driving},
-      author={Gupta, Pranay and Biswas, Abhijat and Admoni, Henny and Held, David},
-      journal={arXiv preprint arXiv:2312.02467},
-      year={2023}
-    }
-  img: ../pics/gupta2023object.png
-  links: 
-      '[arXiv]': https://arxiv.org/abs/2312.02467
-      '[Code]': https://github.com/vehicle-importance/oiecr
-  short_id: gupta2023object
-  site: https://vehicle-importance.github.io/
-  title: "Object Importance Estimation using Counterfactual Reasoning for Intelligent Driving"
-  venue: Robotics and Automation Letters (RAL), 2024
-  video_embed: null
-  tags: 
-      - auto_driving
-
 - abs: "Many robot manipulation tasks can be framed as geometric reasoning tasks, where an agent must be able to precisely manipulate an object into a position that satisfies the task from a set of initial conditions. Often, task success is defined based on the relationship between two objects - for instance, hanging a mug on a rack. In such cases, the solution should be equivariant to the initial position of the objects as well as the agent, and invariant to the pose of the camera. This poses a challenge for learning systems which attempt to solve this task by learning directly from high-dimensional demonstrations - the agent must learn to be both equivariant as well as precise, which can be challenging without any inductive biases about the problem. In this work, we propose a method for precise relative pose prediction which is provably SE(3)-equivariant, can be learned from only a few demonstrations, and can generalize across variations in a class of objects. We accomplish this by factoring the problem into learning an SE(3) invariant task-specific representation of the scene and then interpreting this representation with novel geometric reasoning layers which are provably SE(3) equivariant. We demonstrate that our method can yield substantially more precise placement predictions in simulated placement tasks than previous methods trained with the same amount of data, and can accurately represent relative placement relationships data collected from real-world demonstrations."
   authors: Ben Eisner, Yi Yang, Todor Davchev, Mel Vecerik, Jonathan Scholz, David Held
   award: null