Arrive and Survive: Scaling Safe Goal-Conditioned Policy Learning from One-Bit Failure Signals

Guopeng Li; Yiyang Duan; Yiru Jiao; Chengcheng Xu

cs.LGAug 27, 2026

Arrive and Survive: Scaling Safe Goal-Conditioned Policy Learning from One-Bit Failure Signals

Authors: Guopeng Li, Yiyang Duan, Yiru Jiao, Chengcheng Xu

Organizations: Southeast University Nanjing, China · Lab, Yinwang Intel. Tech. Co. Ltd. Shanghai, China

Abstract

Contrastive reinforcement learning (CRL) scales effectively in goal-conditioned tasks by casting policy learning into a self-supervised contrastive objective. However, in a failure-terminated Markov decision process, established CRL considers pre-failure future goals only when constructing positive samples, without accounting for the probability mass removed by failure termination. Our theoretical analysis shows that this omission induces a systematic overestimation bias in goal-reaching values. Consequently, near-failure trajectories provide disproportionately strong supervision of success despite retaining little future occupancy. Unsafe actions can thereby be reinforced through catastrophic failure bootstrapping, leading to failed policy learning and unsustainable goal-reaching behaviours. To address this problem, we introduce two minimal yet strong corrections: mass-weighted InfoNCE corrects the overweighting of short surviving futures in critic learning, and a log-survival-mass score restores the missing survival mass in policy optimization. The resulting method, Safe Contrastive Reinforcement Learning (Safe-CRL), requires only the one-bit signal provided by failure termination to scale safe goal-conditioned policy learning. Across twelve failure-prone robot navigation and locomotion tasks, Safe-CRL consistently improves survival and substantially outperforms the Scaling-CRL baseline in goal-reaching performance. Additionally, deep Safe-CRL policies exhibit complex failure-avoidance behaviours. This study completes the CRL theory under failure termination and provides a scalable safe RL framework. The code is available via https://github.com/RomainLITUD/safe-crl.

Figures & tables

Figure 1: Comparison of time at goal between Scaling-CRL and the proposed Safe-CRL method in failure-prone navigation and locomotion tasks (mean

\pm

standard deviation over five random seeds). Both methods use 64-layer actor and critic networks (8-layer for Point Goal and Car Goal). Safe-CRL achieves higher or on-par mean time at goal across all tasks, with pronounced gains in nine.

Figure 2: Examples of unsustainable goal reaching under Scaling-CRL. (a) The Point agent reaches the goal region but subsequently collides with a moving obstacle; (b) the Ant reaches the goal at high speed and overshoots into the wall; (c–d) locomotion agents reach the goal but fall immediately afterwards. These examples illustrate that successful short-term goal reaching does not necessarily lead to a stable trajectory under failure termination.

Figure 3: Failure termination removes part of the discounted future-goal occupancy. Established CRL normalizes the remaining pre-failure futures, whereas Safe-CRL corrects the trajectory-wise normalization distortion in critic learning and restores the missing survival mass in actor updates.

Figure 4: Illustrations of the experimental environments. The goal is represented by a bright green cylinder (for Point and Car Goal) or a meshed sphere (for Ant and Humanoid locomotion tasks). The walls in Maze environments are concrete, while other obstacles, hazards (pitfalls), and moving gremlins (purple spheres in Point Goal and Car Goal) are non-colliding (ghost objects). Contacting maze walls is not a failure; entering a designated task-specific failure region triggers failure termination. The goal is randomly respawned only for Point Goal and Car Goal. The other environments use a fixed commanded goal in each episode.

Figure 5: Survival time (top two rows) and goal coverage (bottom two rows) on the 12-task benchmark (mean

\pm

std).

Figure 6: Time at goal and survival time for Safe-CRL using different actor and critic depths. The Z-encoder depth is 4. Point Goal and Car Goal are omitted from the depth-scaling study because they are easier than locomotion.

Figure 7: Predicted survival mass versus survival time in 128 evaluation runs. Actor and critic depths are 64.

Figure 8: Visualization of the policy learned by Safe-CRL. Each row contains 5 snapshots captured from one evaluation episode, showing the key failure-avoidance behaviours of the agent.

			Car Goal		Humanoid Goal		Ant Big Maze		Ant Hardest Pitfall
Method	MW	$\log Z$	Time at goal	Survival time	Time at goal	Survival time	Time at goal	Survival time	Time at goal	Survival time
Scaling-CRL	✗	✗	2.2 $\pm$ 0.1	464.9 $\pm$ 11.2	13.8 $\pm$ 1.0	113.9 $\pm$ 5.3	89.7 $\pm$ 34.2	261.1 $\pm$ 33.1	98.5 $\pm$ 17.1	381.1 $\pm$ 11.6
MW only	✓	✗	2.2 $\pm$ 0.1	471.4 $\pm$ 13.3	28.3 $\pm$ 0.7	146.1 $\pm$ 10.2	91.4 $\pm$ 31.1	291.6 $\pm$ 51.6	113.2 $\pm$ 19.3	411.0 $\pm$ 27.1
$\log Z$ only	✗	✓	2.4 $\pm$ 0.1	537.1 $\pm$ 61.3	288.7 $\pm$ 21.6	397.0 $\pm$ 21.8	337.0 $\pm$ 28.9	693.5 $\pm$ 33.7	271.2 $\pm$ 28.3	722.9 $\pm$ 32.6
Safe-CRL	✓	✓	2.4 $\pm$ 0.2	555.5 $\pm$ 49.1	297.0 $\pm$ 19.4	413.4 $\pm$ 23.9	352.9 $\pm$ 11.1	725.7 $\pm$ 31.7	313.0 $\pm$ 50.7	787.9 $\pm$ 51.1

Table 1: Core component ablation of Safe-CRL. MW denotes MW-InfoNCE. Results are averaged over the final 10% of training and reported as mean

\pm

standard deviation over three random seeds. The actor and critic depths are 16 (8 for Car Goal), and the Z-encoder depth is 4.

Figure 9: Ablations on the coefficient

\beta

\log Z

. The left four panels report time at goal, and the right four panels report survival time. We set the actor and critic depths to 16 layers (8 for Car Goal) and vary

\beta

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Environment	Agent	Goal Respawn	Failure Termination
Point Goal	Point	Yes	Enter obstacle, hazard, or gremlin region
Car Goal	Car	Yes	Enter obstacle, hazard, or gremlin region
Ant Goal	Ant	No	Fall
Humanoid Goal	Humanoid	No	Fall
Ant H-Maze	Ant	No	Fall
Ant Cross Maze	Ant	No	Fall

Appendix

Table B.1: Summary of the experimental environments and failure mechanisms.

Actuator gear	default	ours
abdomen_y	350	100
abdomen_z	350	100
abdomen_x	350	100
right_hip_x	350	100
right_hip_z	350	100
right_hip_y	350	300

Appendix

Table B.2: Humanoid actuator gears under the default setting and our experiments.

Figure C.1: Comparison with SAC-HER on four representative failure-prone tasks. Curves report time at goal and survival time. Safe-CRL uses 16 layers for the actor and critic networks (8 for Car Goal) and 4 layers for the Z-encoder.

	Time at goal		Survival time		Goal coverage (%)
Environment	Scaling-CRL	Safe-CRL	Scaling-CRL	Safe-CRL	Scaling-CRL	Safe-CRL
Point Goal	$1.94\pm 0.11$	$\mathbf{1.98\pm 0.09}$	$555.1\pm 12.3$	$\mathbf{631.5\pm 21.0}$	$81.6\pm 3.4$	$\mathbf{83.0\pm 1.1}$
Car Goal	$2.17\pm 0.04$	$\mathbf{2.39\pm 0.06}$	$464.9\pm 6.5$	$\mathbf{550.1\pm 15.9}$	$74.2\pm 1.9$	$\mathbf{79.7\pm 0.7}$
Ant Goal	$276.83\pm 12.81$	$\mathbf{684.00\pm 14.57}$	$422.1\pm 16.2$	$\mathbf{910.8\pm 8.0}$	$\mathbf{87.2\pm 0.9}$	$81.0\pm 1.6$
Humanoid Goal	$22.19\pm 2.64$	$\mathbf{266.43\pm 20.70}$	$145.7\pm 12.9$	$\mathbf{386.3\pm 19.9}$	$60.9\pm 0.7$	$\mathbf{78.5\pm 0.9}$
Ant H-Maze	$390.60\pm 52.80$	$\mathbf{412.96\pm 17.92}$	$586.6\pm 66.6$	$\mathbf{795.6\pm 19.6}$	$\mathbf{58.3\pm 4.6}$	$54.0\pm 2.5$

Appendix

Table D.1: Final performance on the main benchmark, reported as mean

\pm

standard deviation over five random seeds.

Figure D.1: Comparison of survival time with respawned and fixed goals in Point Goal and Car Goal. Percentages show the mean advantage of Safe-CRL over Scaling-CRL during the final 10% of training.

Figure D.2: Ablation on Z-encoder depth. Actor and critic depths are fixed at 16 layers.

Figure D.3: Ablations on TD-learned Z. Actor and critic depths are set to 16 layers (8 for Car Goal).

Environment	Scaling-CRL	Safe-CRL (ours)
Point Goal	2.92 h	3.07 h
Car Goal	8.73 h	8.94 h
Ant Goal	6.65 h	6.71 h
Humanoid Goal	7.28 h	7.74 h
Ant H-Maze	10.20 h	10.58 h
Ant Cross Maze	10.48 h	10.64 h

Appendix

Table D.2: Average wall-clock training time per 100M environment steps.

Panel of Figure 19 — Figure D.4: Success rate for goals at different places in the playground. Darker colors indicate lower goal-reaching success rates. The robot initially faces the +x axis direction ( $\rightarrow$ ). The diamond marks the initial position of the robot.

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