# Role of reward shaping in object-goal navigation

arXiv: https://arxiv.org/abs/2207.08021 · HTML: https://mlsanity.com/paper/2207.08021/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2022.

## Built on by (most cited first, 1 of 1)

- [Driving Beyond Privilege: Distilling Dense-Reward Knowledge into Sparse-Reward Policies](https://mlsanity.com/paper/2512.04279/) (2025)

## Abstract (arXiv)

Deep reinforcement learning approaches have been a popular method for visual navigation tasks in the computer vision and robotics community of late. In most cases, the reward function has a binary structure, i.e., a large positive reward is provided when the agent reaches goal state, and a negative step penalty is assigned for every other state in the environment. A sparse signal like this makes the learning process challenging, specially in big environments, where a large number of sequential actions need to be taken to reach the target. We introduce a reward shaping mechanism which gradually adjusts the reward signal based on distance to the goal. Detailed experiments conducted using the AI2-THOR simulation environment demonstrate the efficacy of the proposed approach for object-goal navigation tasks.